Object Detection Rate Research Articles

Transformer-CNN for small image object detection

Object recognition in computer vision technology has been a popular research field in recent years. Although the detection success rate of regular objects has achieved impressive results, small object detection (SOD) is still a challenging issue. In the Microsoft Common Objects in Context (MS COCO) public dataset, the detection rate of small objects is typically half that of regular-sized objects. The main reason is that small objects are often affected by multi-layer convolution and pooling, leading to insufficient details to distinguish them from the background or similar objects, resulting in poor recognition rates or even no results. This paper presents a network architecture, Transformer-CNN, that combines a self-attention mechanism-based transformer and a convolutional neural network (CNN) to improve the recognition rate of SOD. It captures global information through a transformer and uses the translation invariance and translation equivalence of CNN to maximize the retention of global and local features while improving the reliability and robustness of SOD. Our experiments show that the proposed model improves the small object recognition rate by 2∼5 % than the general transformer architectures.

An ALMA CO(1-0) survey of the 2Jy sample: large and massive molecular discs in radio AGN host galaxies

ABSTRACT The jets of radio AGN provide one of the most important forms of active galactic nuclei (AGN) feedback, yet considerable uncertainties remain about how they are triggered. Since the molecular gas reservoirs of the host galaxies can supply key information about the dominant triggering mechanism(s), here we present Atacama Large Millimeter/sub-millimeter Array CO(1-0) observations of a complete sample of 29 powerful radio AGN ($P_{1.4\,{\rm GHz}} \gt 10^{25}$ W Hz$^{-1}$ and $0.05 \lt z \lt 0.3$) with an angular resolution of about 2–3 arcsec (corresponding to 2–8 kpc). We detect molecular gas with masses in the range $10^{8.9} \lt M_{{\rm H}_2} \lt 10^{10.2}$ M$_\odot$ in the early-type host galaxies of ten targets, while for the other 19 sources, we derive upper limits. The detection rate of objects with such large molecular masses – $34\pm 9$ per cent – is higher than in the general population of non-active early-type galaxies (ETGs: $\lt $10 per cent). The kinematics of the molecular gas are dominated in most cases by rotating disc-like structures, with diameters up to 25 kpc. Compared with the results for samples of quiescent ETG in the literature, we find a larger fraction of more massive, more extended and less settled molecular gas structures. In most of the CO-detected sources, the results are consistent with triggering of the AGN as the gas settles following a merger or close encounter with a gas-rich companion. However, in a minority of objects at the centres of rich clusters of galaxies, the accretion of gas cooling from the hot X-ray haloes is a plausible alternative to galaxy interactions as a triggering mechanism.

Car Damage Detection with VGG-16

In today's traffic accidents, assessing the damage and keeping damage records quickly is necessary. It is crucial to accelerate damage assessment studies to prevent traffic congestion caused by accidents and open the road to traffic quickly. Deep learning technologies provide various advantages in calculating the magnitude of the damage, displaying the damage situation, and making inferences about the material extent of the damage. In this study, a decision support system is aimed not only for insurance companies or official institutions to see the results but also for the end user and to reveal the damage class of the accidents. The software offered aims to provide an objective perspective not only in accident processes but also in quickly determining the financial value of the vehicle when buying and selling the vehicle. In this study, the training phase of our VGG16-based model, a CNN sub-model, was developed on the data set we obtained from the Kaggle platform (5757 images). With VGG16, our vehicle object detection rate is 98%, the accuracy rate of whether the vehicle is damaged is 90%, the results obtained in the training in which we detect the damaged area is 70%, and finally, the accuracy rate in determining the level of damage (low, medium and high) is 66% has been obtained.

Detecting Moving Objects in Photometric Images Using 3D Hough Transform

In response to the exponential growth of space debris, an increasing number of observation devices are being used for the observation of moving objects, such as space debris and asteroids, which require further improvements in data-processing capabilities for the detection of moving objects. In this study, we propose a rapid detection algorithm designed for detecting moving objects, leveraging the power of the 3D Hough transform. By the simulated image experiments, our results show that the detection rate increases with the number of continuous images when fully extracting objects. Based on this foundation, the object detection rate is at least 87% regardless of the object number in the image sequence when detecting objects from at least six continuous images. In the observed image experiments, we used source-extractor to extract sources. The results show the method can successfully detect objects with signal-to-noise ratio higher than three from sidereal tracking images and can identify asteroids from asteroid tracking images while maintaining a detection speed that meets the requirements for real-time processing.

RIOD:Reinforced Image-based Object Detection for Unruly Weather Conditions

Deep Neural Network (DNN) object detectors have proved their efficiency in the detection and classification of objects in normal weather. However, these models suffer a lot during bad weather conditions (foggy, rain, haze, night, etc.). This study presents a new scheme to reduce the aforementioned issue by attenuating the noise in the input image before feeding it to any kind of neural network-based object detector. In this study, the image optimization function transforms subpar-quality images due to bad weather into pictures with the optimal possible quality by estimating the proper illumination and transmission function. These optimized images showed improved object detection rates in the YOLOv4 and YOLOv5 models. This improvement in object detection was also noticed in the case of video input. This scheme was tested with images/videos from various weather conditions, and the results showed an encouraging improvement in detection rates.

Deep CNN Based Multi Object Detection And Tracking In Video Frames With Mean Distributed Feature Set

The computer vision field of object identification and tracking has been put to use in a broad variety of settings, from medical monitoring to autonomous vehicle navigation to anomaly detection and beyond. The efficiency of object detectors and trackers has substantially increased with the rapid growth of Deep Learning (DL) networks and the computational power of GPUs. Due to their poor generalization performance, classic extracting feature models are limited in their ability to recognize many targets in complicated scenarios, relying instead on low-level feature information such as contour information and texture information. In addition to gleaning granular texture features from pre-level Convolution Neural Networks (CNN), these models trained with deep learning can also glean meta-level knowledge from the post-level convolution layer. This research proposes a Deep CNN based Multi Object Detection and Tracking in Video Frame with Mean Distributed Feature Set (DCNN-MODT-MDFS) model for automatically assigning input objects to one of several predefined categories. Following object detection and tracking, the suggested techniques assign a class label by analyzing numerous frames in a video. The images could be taken from the video frames that are used to train the model. In order to account for the disparity in aspect ratio, the proposed method employs multi-scale feature maps for object detection and uses filters with unique default boxes. The model is trained until there is a noticeable drop in the error rate. The proposed model multi object detection rate and tracking rate is contrasted with the existing models and the results represent that proposed model performance is high.

Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification.

The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%.

UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios.

Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. However, the high proportion of small objects in UAV images and the limited platform resources lead to the low accuracy of most of the existing detection models embedded in UAVs, and it is difficult to strike a good balance between detection performance and resource consumption. To alleviate the above problems, we optimize YOLOv8 and propose an object detection model based on UAV aerial photography scenarios, called UAV-YOLOv8. Firstly, Wise-IoU (WIoU) v3 is used as a bounding box regression loss, and a wise gradient allocation strategy makes the model focus more on common-quality samples, thus improving the localization ability of the model. Secondly, an attention mechanism called BiFormer is introduced to optimize the backbone network, which improves the model's attention to critical information. Finally, we design a feature processing module named Focal FasterNet block (FFNB) and propose two new detection scales based on this module, which makes the shallow features and deep features fully integrated. The proposed multiscale feature fusion network substantially increased the detection performance of the model and reduces the missed detection rate of small objects. The experimental results show that our model has fewer parameters compared to the baseline model and has a mean detection accuracy higher than the baseline model by 7.7%. Compared with other mainstream models, the overall performance of our model is much better. The proposed method effectively improves the ability to detect small objects. There is room to optimize the detection effectiveness of our model for small and feature-less objects (such as bicycle-type vehicles), as we will address in subsequent research.

Development of Potato Mass Estimation System Based on Deep Learning

Potato is one of the world’s four major food crops as an important resource cultivated in about 150 countries. As precision agriculture has recently attracted increasing attention for its role in improving productivity, interest in yield monitoring is also increasing. Yield monitoring is a precision agriculture technology, and it can help farmhouse business management in the future by contributing to variable fertilization and supply and demand control. The present study was carried out to develop and evaluate a system that uses machine vision and deep learning technologies to estimate potato mass to monitor potato yield. The system performs object classification using the YOLOv5 algorithm to sort out potatoes among various foreign substances, object tracking using the DeepSORT algorithm to track the sorted potatoes, and volume calculation using the lengths of the major axis and minor axis of the tracked potatoes. The results of analyzing the function of the developed yield monitoring system showed an object detection rate of 95.2% and a weight measurement error of 9%, indicating that the computation load must be reduced through algorithm optimization to improve the accuracy and that error correction needs to be performed based on the potato position within the view angle.

Deep Learning-Based Defect Detection Framework for Ultra High Resolution Images of Tunnels

This study proposes a defect detection framework to improve the performance of deep learning-based detection models for ultra-high resolution (UHR) images generated by tunnel inspection systems. Most of the scanning technologies used in tunnel inspection systems generate UHR images. Defects in real-world images, on the other hand, are noticeably smaller than the image. These characteristics make simple preprocessing applications, such as downscaling, difficult due to information loss. Additionally, when a deep learning model is trained by the UHR images under the limited computational resource for training, problems may occur, including a reduction in object detection rate, unstable training, etc. To address these problems, we propose a framework that includes preprocessing and postprocessing of UHR images related to image patches rather than focusing on deep learning models. Furthermore, it includes a method for supplementing problems according to the format of the data annotation in the preprocessing process. When the proposed framework was applied to the UHR images of a tunnel, the performance of the deep learning-based defect detection model was improved by approximately 77.19 percentage points (pp). Because the proposed framework is for general UHR images, it can effectively recognize damage to general structures other than tunnels. Thus, it is necessary to verify the applicability of the defect detection framework under various conditions in future works.

High-Speed Object Recognition Based on a Neuromorphic System

Neuromorphic systems are bio-inspired and have the potential to break through the bottleneck of existing intelligent systems. This paper proposes a neuromorphic high-speed object recognition method based on DVS and SpiNNaker and implements a system in which an OR logic aggregation algorithm is used to acquire sufficient effective information and the asynchronous sparse computing mechanism of SNNs is exploited to reduce the computation. The experiment’s results show that the object detection rate of the designed system is more than 99% at the rotating speed of 900~2300 rpm; its response time is within 2.5 ms; and it requires 96.3% less computation than traditional recognition systems using the same scaled ANN.

MCA-YOLOV5-Light: A Faster, Stronger and Lighter Algorithm for Helmet-Wearing Detection

It is an essential measure for workers to wear safety helmets when entering the construction site to prevent head injuries caused by object collision and falling. This paper proposes a lightweight algorithm for helmet-wearing detection based on YOLOV5, which is faster and more robust for helmet detection in natural construction scenarios. In this paper, the MCA attention mechanism is embedded in the backbone network to help the network extract more productive information, reduce the missed detection rate of small helmet objects and improve detection accuracy. In order to ensure the safety of workers in construction, it is necessary to detect whether the construction workers are wearing safety helmets in real-time to achieve monitoring on-site. A channel pruning strategy is proposed on the MCA-YOLOv5 algorithm to compress it, realizing the optimal large-scale model into ultrasmall models for real-time detection on embedded or mobile devices. The experimental results on the public data set show that the model parameter volume is reduced by 87.2%, and the detection speed is increased by 53.5%, even though the MCA-YOLOv5-light reduces the mAP slightly.

CHMM Object Detection Based on Polygon Contour Features by PSM.

Since the conventional split–merge algorithm is sensitive to the object scale variance and splitting starting point, a piecewise split–merge polygon-approximation method is proposed to extract the object contour features. Specifically, the contour corner is used as the starting point for the contour piecewise approximation to reduce the sensitivity of the contour segment for the starting point; then, the split–merge algorithm is used to implement the polygon approximation for each contour segment. Both the distance ratio and the arc length ratio instead of the distance error are used as the iterative stop condition to improve the robustness to the object scale variance. Both the angle and length as two features describe the shape of the contour polygon; they have a strong coupling relationship since they affect each other along the contour order relationship. To improve the description correction of the contour, these two features are combined to construct a Coupled Hidden Markov Model to detect the object by calculating the probability of the contour feature. The proposed algorithm is validated on ETHZ Shape Classes and INRIA Horses standard datasets. Compared with other contour-based object-detection algorithms, the proposed algorithm reduces the feature number and improves the object-detection rate.

An Extremely Effective Spatial Pyramid and Pixel Shuffle Upsampling Decoder for Multiscale Monocular Depth Estimation.

To estimate the accurate depth from a single image, we proposed a novel and effective depth estimation architecture to solve the problem of missing and blurred contours of small objects in the depth map. The architecture consists of Extremely Effective Spatial Pyramid modules (EESP) and Pixel Shuffle upsampling Decoders (PSD). The results of this study show that multilevel information and the upsampling method in the decoders are essential for recovering the accurate depth map. Through the model we proposed, competitive performance compared with state-of-the-art methods in terms of reconstruction of object boundaries and the detection rate of small objects has been demonstrated. Our approach has wide applications in higher-level visual tasks, including 3D reconstruction and autonomous driving.

Dog Behavior Recognition Based on Multimodal Data from a Camera and Wearable Device

Although various studies on monitoring dog behavior have been conducted, methods that can minimize or compensate data noise are required. This paper proposes multimodal data-based dog behavior recognition that fuses video and sensor data using a camera and a wearable device. The video data represent the moving area of dogs to detect the dogs. The sensor data represent the movement of the dogs and extract features that affect dog behavior recognition. Seven types of behavior recognition were conducted, and the results of the two data types were used to recognize the dog’s behavior through a fusion model based on deep learning. Experimentation determined that, among FasterRCNN, YOLOv3, and YOLOv4, the object detection rate and behavior recognition accuracy were the highest when YOLOv4 was used. In addition, the sensor data showed the best performance when all statistical features were selected. Finally, it was confirmed that the performance of multimodal data-based fusion models was improved over that of single data-based models and that the CNN-LSTM-based model had the best performance. The method presented in this study can be applied for dog treatment or health monitoring, and it is expected to provide a simple way to estimate the amount of activity.

Automated detection and classification of synoptic-scale fronts from atmospheric data grids

Abstract. Automatic determination of fronts from atmospheric data is an important task for weather prediction as well as for research of synoptic-scale phenomena. In this paper we introduce a deep neural network to detect and classify fronts from multi-level ERA5 reanalysis data. Model training and prediction is evaluated using two different regions covering Europe and North America with data from two weather services. We apply label deformation within our loss function, which removes the need for skeleton operations or other complicated post-processing steps as used in other work, to create the final output. We obtain good prediction scores with a critical success index higher than 66.9 % and an object detection rate of more than 77.3 %. Frontal climatologies of our network are highly correlated (greater than 77.2 %) to climatologies created from weather service data. Comparison with a well-established baseline method based on thermodynamic criteria shows a better performance of our network classification. Evaluated cross sections further show that the surface front data of the weather services as well as our network classification are physically plausible. Finally, we investigate the link between fronts and extreme precipitation events to showcase possible applications of the proposed method. This demonstrates the usefulness of our new method for scientific investigations.

A Novel Power-Saving Reversing Camera System with Artificial Intelligence Object Detection

According to a study by the Insurance Institute for Highway Safety (IIHS), the driving collision rate of using only the reversing camera system is lower than that of using both the reversing camera system and the reversing radar. In this article, we implemented a reversing camera system with artificial intelligence object detection to increase the information of the reversing image. Our system consists of an image processing chip (IPC) with wide-angle image distortion correction and an image buffer controller, a low-power KL520 chip and an optimized artificial intelligence model MobileNetV2-YOLOV3-Optimized (MNYLO). The results of the experiment show the three advantages of our system. Firstly, through the image distortion correction of IPC, we can restore the distorted reversing image. Secondly, by using a public dataset and collected images of various weathers for artificial intelligence model training, our system does not need to use image algorithms that eliminate bad weathers such as rain, fog, and snow to restore polluted images. Objects can still be detected by our system in images contaminated by weather. Thirdly, compared with the AI model Tiny_YOLOV3, not only the parameters of our MNYLO have been reduced by 72.3%, the amount of calculation has been reduced by 86.4%, but the object detection rate has also been maintained and avoided sharp drops.

A Method to Compute the Schrieffer-Wolff Generator for Analysis of Quantum Memory.

Quantum illumination uses entangled light that consists of signal and idler modes to achieve higher detection rate of a low-reflective object in noisy environments. The best performance of quantum illumination can be achieved by measuring the returned signal mode together with the idler mode. Thus, it is necessary to prepare a quantum memory that can keep the idler mode ideal. To send a signal towards a long-distance target, entangled light in the microwave regime is used. There was a recent demonstration of a microwave quantum memory using microwave cavities coupled with a transmon qubit. We propose an ordering of bosonic operators to efficiently compute the Schrieffer–Wolff transformation generator to analyze the quantum memory. Our proposed method is applicable to a wide class of systems described by bosonic operators whose interaction part represents a definite number of transfer in quanta.

CNN-SVM based vehicle detection for UAV platform

Conventional surveillance devices are deployed at fixed locations on road sideways, poles or on traffic lights, which provide a constant and fixed surveillance view of the urban traffic. Unmanned aerial vehicles (UAVs) have for last two decades received considerable attention in building smart and effective system with wider coverage using low cost, highly flexible unmanned platform for smart city infrastructure. Unlike fixed monitoring devices, the camera platform of aerial vehicles has many constraints, as it is in constant motion including titling and panning, and thus makes it difficult to process data for real time applications. The inaccuracy in object detection rates from UAV videos has motivated the research community to combine different approaches such as optical flow and supervised learning algorithms. The method proposed in this research incorporates steps that include Kanade-Lucas optical flow method for moving object detection, building connected graphs to isolate objects and convolutional neural network (CNN), followed by support vector machine (SVM) for final classification. The generated optical flow contains background (and tiny) objects detected as vehicle as the camera platform moves. The classifier introduced here rules out the presence of any other (moving) objects to be detected as vehicles. The methodology adopted is tested on a stationary and moving aerial videos. The system is shown to have performance accuracy of 100% in case of stationary video and 98% in case of video from aerial platform.

Block Detection and Tracking Algorithm of Foreign Objects Debris in Airport Runway Based on Background Alignment and Difference

<p indent=0mm>Aiming at the problems of unmanned detection and recognition of foreign object debris (FOD) in the autonomous detection and recognition of airport runways, the detection results are affected by the environment, the detection of small targets is difficult, and the rate of missed detection is high. An airport runway foreign object debris sub-block detection and tracking method based on background difference is proposed in this article. First, the linear interpolation of position information assisted by the speed is used to obtain the most relevant frame of the image in background image template library and the image sequence to be identified. Then, the image is divided into different sub-blocks, the adaptive weights are set using the image texture complexity in each sub-block, and the feature point extraction is combined with the oriented fast and rotated brief (ORB) method to normalize the sub-block feature points to the original image. Align the image with the most relevant frame of the background template library using the feature points result before to obtain the foreign object detection result. Finally, the kernelized correlation filter is introduced to track the detection results for multiple targets. Local tracking methods of each sub-block are used to reduce the computation time and the tracking results are tested for reliability. According to the experiment results in three different scenarios, compared with another five common image-based airport FOD detection algorithms, this method can reduce the overall false detection rate of foreign objects by more than 70%, and the overall missed detection rate to 0 when the size of foreign body is larger than <sc>1 cm<inline-formula id=inline1><mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mo>×</mml:mo></mml:math></inline-formula>1 cm,</sc> while ensuring the algorithm processing speed, which achieved good results.