Mask R-CNN Algorithm Research Articles

Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network.

Mosquito-borne diseases cause a huge burden on public health worldwide. The viruses that cause these diseases impact the behavioural traits of mosquitoes, including locomotion and feeding. Understanding these traits can help in improving existing epidemiological models and developing effective mosquito traps. However, it is difficult to understand the flight behaviour of mosquitoes due to their small sizes, complicated poses, and seemingly random moving patterns. Currently, no open-source tool is available that can detect and track resting or flying mosquitoes. Our work presented in this paper provides a detection and trajectory estimation method using the Mask RCNN algorithm and spline interpolation, which can efficiently detect mosquitoes and track their trajectories with higher accuracy. The method does not require special equipment and works excellently even with low-resolution videos. Considering the mosquito size, the proposed method's detection performance is validated using a tracker error and a custom metric that considers the mean distance between positions (estimated and ground truth), pooled standard deviation, and average accuracy. The results showed that the proposed method could successfully detect and track the flying (≈ 96% accuracy) as well as resting (100% accuracy) mosquitoes. The performance can be impacted in the case of occlusions and background clutters. Overall, this research serves as an efficient open-source tool to facilitate further examination of mosquito behavioural traits.

Loess landslides detection via a partially supervised learning and improved Mask-RCNN with multi-source remote sensing data

Loess landslides pose a severe threat of destruction, and detecting them is crucial for minimizing their impact on society. They typically consist of wind-deposited clay and silt, which makes them challenging to detect using conventional methods. Techniques like visual interpretation and field surveys are the most useful, yet these methods can be laborious, expensive, and require a certain level of prior knowledge. Furthermore, remote sensing approaches face the challenge of distinguishing between natural erosion and landslides. Recently, deep learning for landslide detection has the potential to speed up and improve detection accuracy. Nonetheless, deep learning models require large amounts of labeled data and the development of robust algorithms capable of extracting meaningful features from remote sensing data. In this article, a novel approach is introduced to improve the Mask Regional Convolutional Neural Network (Mask-RCNN) algorithm for accurately detecting landslides when the number of available segmentation mask samples is limited. Specifically, A novel loess landslides dataset is established using high-resolution remote sensing images in Gansu Province. In the context of partially supervised learning, a neural network branch containing a weight transfer function is developed to capture mask information from bounding boxes. Additionally, a mask-scoring network block is used to learn the quality of predicted instance masks. Our modified algorithm achieves an average precision improvement of 20.7% compared to the original Mask R-CNN algorithm in small landslide detection. The mask IoU threshold value of 0.5 is used to estimate the average accuracy higher than 0.75. The average precision of the segmentation mask is improved by 16.7% in test set. By proposing a solution that can achieve accurate landslide detection while using limited labeled samples, this study makes a valuable contribution to the application of deep learning in the domains of remote sensing and landslide detection.

Применение нейронных сетей для распознавания объектов на железнодорожном транспорте

Purpose: With the help of vision systems and neural networks, such as YOLOv8 and MASK R-CNN, it is possible to quickly and accurately detect objects that can lead to an accident or delay trains. YOLOv8 is one of the most popular real-time object detection algorithms that uses deep neural networks to classify and localize objects. YOLOv8 can detect objects in images and videos with high speed and accuracy. This model can work on various hardware platforms, including mobile devices and computers. MASK R-CNN is an even more advanced object detection algorithm that has the ability to highlight objects and their contours with high accuracy. MASK R-CNN uses convolutional neural networks and mask segmentation techniques to detect objects. It can work both in real time and on static images. When vision systems are equipped with YOLOv8 and MASK R-CNN neural networks, they can quickly respond to extraneous objects that appear on the rails. The purpose of the article is to develop algorithms for detecting railway transport objects and obstacles using technical vision and neural networks, and to evaluate the effectiveness of algorithms. Methods: The YOLOv8 algorithm is based on the architecture of convolutional neural networks and uses supervised learning methods. This model takes an image as input and provides estimates of the probability that a certain object is present in the image in real time. To achieve this, YOLOv8 employs region of interest (ROI) detection methods, allowing to determine the areas of the image on which objects may be located. The MASK R-CNN algorithm uses more sophisticated methods, such as mask segmentation methods and proportional resizing of the area of interest (RoIAlign) to achieve more accurate results of object detecting in images and videos. It is also based on convolutional neural networks and uses supervised learning methods. MASK R-CNN uses mask segmentation methods to determine the contour of an object in an image, as well as the RoIAlign method, which allows for superior quality when processing various image sizes. Common mathematical methods that are used in YOLOv8 and MASK R-CNN are methods of convolutional neural network, supervised learning and optimization of the loss function. They are based on deep learning algorithms such as stochastic gradient descent and backward propagation of errors. Results: An algorithm for detecting foreign objects on the route of rolling stock using a technical vision system, calculation of the evaluation of the quality of neural networks performance, error matrices have been formed, the results of neural network processing have been obtained. Practical significance: An algorithm for detecting foreign objects on the route of the moving rolling stock using a technical vision system has been developed, two neural networks have been trained to detect railway transport objects and obstacles on the way.

An innovative application of pantograph recognition system based on deep learning

One of the most significant aspects for the correct operation of a modern railway electrification system is the health of the pantograph and overhead line. The application of machine vision technology to monitor the status of pantographs in real time can reduce pantographs and catenary accidents caused by unpredictable events. It is challenging to achieve real-time and accurate criteria with present pantograph detecting technologies. Therefore, this methodology collects pantograph images through high-definition cameras and transmits them to the cloud through 5G, use the Mask R-CNN algorithm to process and analyse the images., This technology can assist railway technicians in judging the status of the pantograph. Mask R-CNN employs the Resnet network for feature extraction. Resnet has the characteristics of cross-layer connection, which avoids the problem of network degradation due to the deep learning network being too deep, and greatly improves the training efficiency. The recognition matching degree of pantographs are greater than 0.975, enabling pantograph recognition in a variety of environmental conditions. The use of 5G connection increases transmission speed and allows for real-time detection of pantograph status, which is critical for the railway's automated operation.

Military camouflage classification with Mask R-CNN algorithm

Camouflage, which is used as an art of hiding by living things in nature, started to be used in the military field in the 19th century with the widespread use of long-range firearms. When factors such as different nations, environment and climate are considered, we come across camouflages in various colors and patterns. Over time, the camouflage patterns adopted and used by countries or unions have become national identity. This study is on the classification and segmentation of camouflaged soldiers of 5 countries with deep learning. While the similarity of the camouflaged area with the background makes segmentation difficult, it becomes difficult to classify each camouflage pattern due to the cut of the fabric and the different locations of the pattern pieces on each soldier. There are different studies in the literature that are referred to as camouflage or pattern classification. The mentioned studies are in the form of segmentation of camouflaged object or classification of camouflaged objects of different types. Since the segmented and classified objects in this study are camouflaged soldiers, what is expected from the deep learning algorithm is to classify the objects mainly according to the camouflage pattern, not their outlines. In the study, 861 camouflaged soldier images were collected for 5 countries (Türkiye-Azerbaijan, USA, Russia, China, France) and polygonal labeling was made. Türkiye and Azerbaijan are considered a class as they have similar camouflages. For the solution of the problem, military camouflage classification was discussed with the Mask R-CNN algorithm, which is widely used today for object detection, segmentation and classification, and the importance of deep learning algorithms has been proven with such a difficult problem. The training resulted in 0.005219 classification loss and 0.03985 masking loss. The classification and segmentation success rate of the study is 95%.

Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT.

To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans. The dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions (BL) and 41 control scans (without lesions), obtained using three CBCT devices with diverse imaging protocols. Lesions were marked in all axial slices by experienced maxillofacial radiologists. All cases were divided into sub-datasets: training (20,214 axial images), validation (4530 axial images), and testing (6795 axial images). A Mask-RCNN algorithm segmented the bone lesions in each axial slice. Analysis of sequential slices was used for improving the Mask-RCNN performance and classifying each CBCT scan as containing bone lesions or not. Finally, the algorithm generated 3D segmentations of the lesions and calculated their volumes. The algorithm correctly classified all CBCT cases as containing bone lesions or not, with an accuracy of 100%. The algorithm detected the bone lesion in axial images with high sensitivity (95.9%) and high precision (98.9%) with an average dice coefficient of 83.5%. The developed algorithm detected and segmented bone lesions in CBCT scans with high accuracy and may serve as a computerized tool for detecting incidental bone lesions in CBCT imaging. Our novel deep-learning algorithm detects incidental hypodense bone lesions in cone beam CT scans, using various imaging devices and protocols. This algorithm may reduce patients' morbidity and mortality, particularly since currently, cone beam CT interpretation is not always preformed. • A deep learning algorithm was developed for automatic detection and 3D segmentation of various maxillofacial bone lesions in CBCT scans, irrespective of the CBCT device or the scanning protocol. • The developed algorithm can detect incidental jaw lesions with high accuracy, generates a 3D segmentation of the lesion, and calculates the lesion volume.

Deep Learning-Based Modified YOLACT Algorithm on Magnetic Resonance Imaging Images for Screening Common and Difficult Samples of Breast Cancer.

Computer-aided methods have been extensively applied for diagnosing breast lesions with magnetic resonance imaging (MRI), but fully-automatic diagnosis using deep learning is rarely documented. Deep-learning-technology-based artificial intelligence (AI) was used in this work to classify and diagnose breast cancer based on MRI images. Breast cancer MRI images from the Rider Breast MRI public dataset were converted into processable joint photographic expert group (JPG) format images. The location and shape of the lesion area were labeled using the Labelme software. A difficult-sample mining mechanism was introduced to improve the performance of the YOLACT algorithm model as a modified YOLACT algorithm model. Diagnostic efficacy was compared with the Mask R-CNN algorithm model. The deep learning framework was based on PyTorch version 1.0. Four thousand and four hundred labeled data with corresponding lesions were labeled as normal samples, and 1600 images with blurred lesion areas as difficult samples. The modified YOLACT algorithm model achieved higher accuracy and better classification performance than the YOLACT model. The detection accuracy of the modified YOLACT algorithm model with the difficult-sample-mining mechanism is improved by nearly 3% for common and difficult sample images. Compared with Mask R-CNN, it is still faster in running speed, and the difference in recognition accuracy is not obvious. The modified YOLACT algorithm had a classification accuracy of 98.5% for the common sample test set and 93.6% for difficult samples. We constructed a modified YOLACT algorithm model, which is superior to the YOLACT algorithm model in diagnosis and classification accuracy.

Improved Mask R-CNN Multi-Target Detection and Segmentation for Autonomous Driving in Complex Scenes.

Vision-based target detection and segmentation has been an important research content for environment perception in autonomous driving, but the mainstream target detection and segmentation algorithms have the problems of low detection accuracy and poor mask segmentation quality for multi-target detection and segmentation in complex traffic scenes. To address this problem, this paper improved the Mask R-CNN by replacing the backbone network ResNet with the ResNeXt network with group convolution to further improve the feature extraction capability of the model. Furthermore, a bottom-up path enhancement strategy was added to the Feature Pyramid Network (FPN) to achieve feature fusion, while an efficient channel attention module (ECA) was added to the backbone feature extraction network to optimize the high-level low resolution semantic information graph. Finally, the bounding box regression loss function smooth L1 loss was replaced by CIoU loss to speed up the model convergence and minimize the error. The experimental results showed that the improved Mask R-CNN algorithm achieved 62.62% mAP for target detection and 57.58% mAP for segmentation accuracy on the publicly available CityScapes autonomous driving dataset, which were 4.73% and 3.96%% better than the original Mask R-CNN algorithm, respectively. The migration experiments showed that it has good detection and segmentation effects in each traffic scenario of the publicly available BDD autonomous driving dataset.

Soybean Disease Detection and Segmentation Based on Mask-RCNN Algorithm

Anthracnose, frogeye leaf spot (FLS), rhizoctonia aerial blight (RAB), soybean mosaic virus (SMV), and yellow mosaic virus (YMV) of soybean are major common soybean leaf diseases that seriously affect soybean yield in India. However, the existing system needs a real-time detection method for soybean leaf diseases, which will help to take appropriate action for disease cure with minimum losses. This study studied a real-time detector for soybean leaf diseases based on deep convolutional neural networks. The 3,127 RGB images of disease-free leaves, anthracnose, FLS, RAB, SMV, and YMV-affected leaves of soybean were collected from the agriculture fields. The Mask R-CNN detection algorithm was used for the detection of soybean leaf diseases by introducing the Res Net 50 module. The pre-processed images (512×512 pixels) were used as input in Mask R-CNN. The model was trained at a number of epochs, training step per epoch training & validation, and learning rate were 80, 500, 50, 8, and 0.001, respectively. The detection accuracy was calculated at three levels of minimum detection confidence i.e. 0.80, 0.85, and 0.90. The results indicate that the maximum detection accuracy i.e. greater than 85% at 0.90 level of minimum detection confidence. This research indicates that the real-time detector based on deep learning provides a feasible solution for diagnosing soybean leaf diseases and provides guidance for the detection of other plant diseases. In addition to that the application of pesticide in the early stage reduce the use of pesticide resulting in less environmental pollution.

MP16-06 MACHINE LEARNING-BASED DECISION SUPPORT SYSTEM TO DISTINGUISH URIC ACID STONES IN PATIENTS WITH KIDNEY STONES OF ′GREY ZONE′ HOUNSFIELD UNITS: INTERNATIONAL MULTICENTER DEVELOPMENT AND EXTERNAL VALIDATION STUDY

You have accessJournal of UrologyCME1 Apr 2023MP16-06 MACHINE LEARNING-BASED DECISION SUPPORT SYSTEM TO DISTINGUISH URIC ACID STONES IN PATIENTS WITH KIDNEY STONES OF ′GREY ZONE′ HOUNSFIELD UNITS: INTERNATIONAL MULTICENTER DEVELOPMENT AND EXTERNAL VALIDATION STUDY Kyochul Koo, Victor K. F. Wong, Abdulghafour Halawani, Sujin Lee, Sangyeop Baek, Hoyong Kang, and Ben H. Chew Kyochul KooKyochul Koo More articles by this author , Victor K. F. WongVictor K. F. Wong More articles by this author , Abdulghafour HalawaniAbdulghafour Halawani More articles by this author , Sujin LeeSujin Lee More articles by this author , Sangyeop BaekSangyeop Baek More articles by this author , Hoyong KangHoyong Kang More articles by this author , and Ben H. ChewBen H. Chew More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003236.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Correct differential diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared with surgical intervention. We developed and validated an autonomic decision-support system (DSS) to distinguish UA stones in patients with kidney stones of Hounsfield units (HU) <800. METHODS: An international, multicenter, cross-sectional study was performed on 176 patients who received percutaneous nephrolithotomy for kidney stones with HU <800. Data from 136 (77.3%) patients were used for model training, validation, and testing (ratio 8:1:1), while data from 40 (22.7%) patients from a transnational institution were used for external validation. Demographic and clinical data consisted of 30 features potentially associated with stone component. A total of 14,843 kidney and stone contour-annotated computed tomography (CT) images were trained with the ResNet-18 Detectron2 Mask R-CNN algorithm to delineate renal anatomy and kidney stones and to measure stone features. Finally, the model was interpreted using the SHAP algorithm to enable visual interpretation of the association between the variables and model output. RESULTS: There were no significant differences in baseline features between the development and validation cohorts. Our model was 100% sensitive in detecting kidney stones in each patient. The delineation of kidney and stone contours was precise within clinically acceptable ranges (Figure 1). The development model provided a predictive performance of 95.9% (92.9% sensitivity, 97.1% specificity). On external validation, the model’s prediction accuracy remained within a clinically acceptable range of 87.9% (66.7% sensitivity, 92.6% specificity). SHAP plots revealed stone density, diabetes mellitus, and urinary pH to be the top features for distinguishing UA stones. CONCLUSIONS: Our development and external validation study show that an automated DSS can conveniently identify and delineate kidney stones and distinguish UA stones from other component stones within the ‘grey zone’ Hounsfield units. Our DSS can be reliably used to select candidates for an earlier-directed alkalization therapy. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e204 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Kyochul Koo More articles by this author Victor K. F. Wong More articles by this author Abdulghafour Halawani More articles by this author Sujin Lee More articles by this author Sangyeop Baek More articles by this author Hoyong Kang More articles by this author Ben H. Chew More articles by this author Expand All Advertisement PDF downloadLoading ...

Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN.

The grade of wheat quality depends on the proportion of unsound kernels. Therefore, the rapid detection of unsound wheat kernels is important for wheat rating and evaluation. However, in practice, unsound kernels are hand-picked, which makes the process time-consuming and inefficient. Meanwhile, methods based on traditional image processing cannot divide adherent particles well. To solve the above problems, this paper proposed an unsound wheat kernel recognition algorithm based on an improved mask RCNN. First, we changed the feature pyramid network (FPN) to a bottom-up pyramid network to strengthen the low-level information. Then, an attention mechanism (AM) module was added between the feature extraction network and the pyramid network to improve the detection accuracy for small targets. Finally, the regional proposal network (RPN) was optimized to improve the prediction performance. Experiments showed that the improved mask RCNN algorithm could identify the unsound kernels more quickly and accurately while handling adhesion problems well. The precision and recall were 86% and 91%, respectively, and the inference time on the test set with about 200 targets for each image was 7.83 s. Additionally, we compared the improved model with other existing segmentation models, and experiments showed that our model achieved higher accuracy and performance than the other models, laying the foundation for wheat grading.

USAGE OF MASK R-CNN FOR AUTOMATIC LICENSE PLATE RECOGNITION

The subject of study is the creation process of an artificial intelligence system for automatic license plate detection. The goal is to achieve high license plate recognition accuracy on large camera angles with character extraction. The tasks are to study existing license plate recognition technics and to create an artificial intelligence system that works on big shooting camera angles with the help of modern machine learning solution – deep learning. As part of the research, both hardware and software-based solutions were studied and developed. For testing purposes, different datasets and competing systems were used. Main research methods are experiment, literature analysis and case study for hardware systems. As a result of analysis of modern methods, Mask R-CNN algorithm was chosen due to high accuracy. Conclusions. Problem statement was declared; solution methods were listed and characterized; main algorithm was chosen and mathematical background was presented. As part of the development procedure, accurate automatic license plate system was presented and implemented in different hardware environments. Comparison of the network with existing competitive systems was made. Different object detection characteristics, such as Recall, Precision and F1-Score, were calculated. The acquired results show that developed system on Mask R-CNN algorithm process images with high accuracy on large camera shooting angles.

Preparing pathological data to develop an artificial intelligence model in the nonclinical study

Artificial intelligence (AI)-based analysis has recently been adopted in the examination of histological slides via the digitization of glass slides using a digital scanner. In this study, we examined the effect of varying the staining color tone and magnification level of a dataset on the result of AI model prediction in hematoxylin and eosin stained whole slide images (WSIs). The WSIs of liver tissues with fibrosis were used as an example, and three different datasets (N20, B20, and B10) were prepared with different color tones and magnifications. Using these datasets, we built five models trained Mask R-CNN algorithm by a single or mixed dataset of N20, B20, and B10. We evaluated their model performance using the test dataset of three datasets. It was found that the models that were trained with mixed datasets (models B20/N20 and B10/B20), which consist of different color tones or magnifications, performed better than the single dataset trained models. Consequently, superior performance of the mixed models was obtained from the actual prediction results of the test images. We suggest that training the algorithm with various staining color tones and multi-scaled image datasets would be more optimized for consistent remarkable performance in predicting pathological lesions of interest.

Multidefect Detection Tool for Large-Scale PV Plants: Segmentation and Classification

Unmanned aerial vehicles (UAVs) with high-resolution optical and infrared ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> ) imaging have been introduced in recent years to perform inexpensive and fast inspections in operation and maintenance activities of solar power plants, reducing the labor needed, while lowering the on-site inspection time. Even though UAVs can acquire images extremely quickly, the analysis of those images is still a time-consuming procedure that should be performed by a trained professional. Therefore, a computer vision approach may be used to accelerate image analysis. In this work, a dataset of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> images was created from a 10-MW solar power plant and a comparative analysis between mask R- convolutional neural network (CNN) and U-Net was performed for two experiments. Concerning the defective module segmentation, the mask R-CNN algorithm achieved a mean average precision at intersection over union (IoU) = 0.50 of 0.96, using augmentation data. Regarding the segmentation and classification of failure type, the algorithm reached a value of 0.88 considering the same evaluation metric and data augmentation. When compared to the U-Net in terms of IoU, the mask R-CNN outperformed it with 0.87 and 0.83 for the first and second experiments, respectively.

Automatic fault detection of utility-scale photovoltaic solar generators applying aerial infrared thermography and orthomosaicking

As large-scale Photovoltaic (PV) power plants are being expanded in installation number and capacity, aerial infrared thermography (aIRT) has proven to be effective in detecting at different phases of their development, construction and commissioning to operation and maintenance. However, evaluating the aerial imagery over hundreds of hectares fields of PV arrays is very time-consuming and subject to human error. This paper proposes a complete framework for automatically detecting faults in large-scale PV power plants and their physical location inside the plant site. To this end, a Mask-RCNN algorithm is developed and fine-tuned for instance segmentation using a dataset of 93 samples collected in an aIRT flight campaign in Brazil. The results are combined with orthomosaic techniques to create an orthomap of the PV system with the highlighted faults. The proposed method has been tested to automatically detect the faults in two power plants. Several tests were performed to improve the algorithm’s accuracy, resulting in high-accuracy results for detecting and localizing hot spots in PV plants and disconnected substrings. The resulting maps could successfully show the location of these faults with high accuracy (10% of false positives).

Object Recognition System for the Visually Impaired: A Deep Learning Approach using Arabic Annotation

Object detection is an important computer vision technique that has increasingly attracted the attention of researchers in recent years. The literature to date in the field has introduced a range of object detection models. However, these models have largely been English-language-based, and there is only a limited number of published studies that have addressed how object detection can be implemented for the Arabic language. As far as we are aware, the generation of an Arabic text-to-speech engine to utter objects’ names and their positions in images to help Arabic-speaking visually impaired people has not been investigated previously. Therefore, in this study, we propose an object detection and segmentation model based on the Mask R-CNN algorithm that is capable of identifying and locating different objects in images, then uttering their names and positions in Arabic. The proposed model was trained on the Pascal VOC 2007 and 2012 datasets and evaluated on the Pascal VOC 2007 testing set. We believe that this is one of a few studies that uses these datasets to train and test the Mask R-CNN model. The performance of the proposed object detection model was evaluated and compared with previous object detection models in the literature, and the results demonstrated its superiority and ability to achieve an accuracy of 83.9%. Moreover, experiments were conducted to evaluate the performance of the incorporated translator and TTS engines, and the results showed that the proposed model could be effective in helping Arabic-speaking visually impaired people understand the content of digital images.

Picture Processing Optimization Technology Based on Mask R-CNN Algorithm

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages.

Tobacco Leaf Segmentation Based on Improved MASK RCNN Algorithm and SAM Model

Tobacco Leaf Segmentation Based on Improved MASK RCNN Algorithm and SAM Model

Identifikasi Objek Cagar Budaya Candi Mahligai Berbasis Citra Digital Menggunakan Mask R-CNN

Object detection is determining the existence of an object and its scope and location in an image. Excessive historical relics and it is generally difficult for humans to remember all the names. One of the fields of science that supports in facilitating human work is confidence score. The main problem in confidence score is how to recognize images and make use of the images they capture. The purpose of this research is to identify the image of Mahligai Temple. This research uses the Deep Learning Mask R-CNN algorithm as a detection of image objects that can recognize cultural heritage sites. The application is carried out on the image of Mahligai Temple. The results using Confussion Matrix and obtained an accuracy of 82.0%, precision 78.57% and recall 88.0%. Single Class identification done successfully at the threshold value 0.776 with error percentage of 18%.

Farmland Soil Block Identification and Distribution Statistics Based on Deep Learning

Soil block distribution is one of the important indexes to evaluate the tillage performance of agricultural machinery. The traditional manual screening methods have the problems of low efficiency and damaging the original surface of the soil. This study proposes a statistical method of farmland soil block distribution based on deep learning. This method combines the adaptive learning rate and squeeze-and-excitation networks channel attention mechanism based on the original Mask-RCNN and uses the improved model to identify, segment and distribute statistics of the farmland soil blocks. Firstly, the influence of different learning rates and an improved Mask-RCNN algorithm model on training results were analyzed. Secondly, the effectiveness of the model in soil block identification and size measurement was analyzed. Finally, the identified soil blocks were classified accordingly, and the scale problem of soil block distribution after removing edge soil blocks was analyzed. The results show that with the decrease of learning rate, the loss value of model training decreases and the prediction accuracy of model is improved. The average precision value of the improved model increased by 25.29 %, and the recall value increased by 8.92%. The correlation coefficient of the maximum diameter measured by manual measurement and the maximum diameter measured by model algorithm was 0.99, which verifies the feasibility of the algorithm model. The prediction error of the model is the smallest when the camera height is 40 cm. Large-scale detection of soil block size in an experimental field in Hefei, Anhui, with an average confidence of over 97%. At the same time, the soil block is effectively classified according to the set classification standard. This study can provide an effective method for the accurate classification of soil block size and can provide a quantitative basis for the control of farmland cultivation intensity.