Fully Convolutional Network Research Articles

MFCANet: A road scene segmentation network based on Multi-Scale feature fusion and context information aggregation

Road scene segmentation is the basic task of autonomous driving. Recent representative scene segmentation methods adopt the full convolutional network based on the encoder-decoder. However, the framework can cause the loss of image fine-grained information in the process of down-sampling, feature extraction and feature fusion, resulting in blurred boundary details and chaotic segmentation effect. In this work, a road scene segmentation network based on multi-scale feature fusion and context information aggregation is proposed, in which context information is used to guide feature fusion and enhance semantic feature extraction. Three plug-and-play modules are designed to extract multi-scale features with strong semantic information from high-level features, which compensate for the loss of spatial information in the upper sampling stage, and capture the information dependence among pixels to improve pixel-by-pixel segmentation. Experimental results on Camvid and Cityscapes show that the proposed multi-scale feature fusion and context information aggregation network (MFCANet) can achieve satisfactory performance compared with the state-of-the-art segmentation methods.

Aspects of image data preparation to extend a classification scheme for cleaning mechanisms to realistic soils

AbstractKnowledge of the cleaning mechanism is necessary to choose a suitable model for a cleaning simulation. In the present work, an existing classification scheme for cleaning mechanisms is considered. Altough this framework is quite promising, the generation of training data constitutes a bottleneck, since the labeling was done manually and very roughly in order to supply the necessary amount of samples in a reasonable time. This, in turn, causes the scheme to be inaccurate when applied to more realistic data. The aim of the present work is to improve the preparation of training data preparation by introducing a semi‐automatic labeling procedure. The labeling procedure involves a new perspective on the data and the application of a gradient filter procedure. Furthermore, fully convolutional networks (FCNs) are employed to generalize different gradient filter. The labeling procedure is significantly faster and more consistent than manual labeling. Also, a proof of concept is provided showing that the FCNs are a suitable technique for the present classification task.

A Method for Calculating the Leaf Area of Pak Choi Based on an Improved Mask R-CNN

The leaf area of pak choi is a critical indicator of growth rate, nutrient absorption, and photosynthetic efficiency, and it is required to be precisely measured for an optimal agricultural output. Traditional methods often fail to deliver the necessary accuracy and efficiency. We propose a method for calculating the leaf area of pak choi based on an improved Mask R-CNN. We have enhanced Mask R-CNN by integrating an advanced attention mechanism and a two-layer fully convolutional network (FCN) into its segmentation branch. This integration significantly improves the model’s ability to detect and segment leaf edges with increased precision. By extracting the contours of reference objects, the conversion coefficient between the pixel area and the actual area is calculated. Using the mask segmentation output from the model, the area of each leaf is calculated. Experimental results demonstrate that the improved model achieves mean average precision (mAP) scores of 0.9136 and 0.9132 in detection and segmentation tasks, respectively, representing improvements of 1.01% and 1.02% over the original Mask R-CNN. The model demonstrates excellent recognition and segmentation capabilities for pak choi leaves. The error between the calculation result of the segmented leaf area and the actual measured area is less than 4.47%. These results indicate that the proposed method provides a reliable segmentation and prediction performance. It eliminates the need for detached leaf measurements, making it suitable for real-life leaf area measurement scenarios and providing valuable support for automated production technologies in plant factories.

YOLO‐UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image

Brain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a clear sight of the shape and boundary of the tumor. The clear shape and boundary of the tumor will increase the probability of safe medical surgery. Analysis of this different scope of image types requires refined computerized quantification and visualization tools. This paper employed deep learning to detect and segment brain tumor MRI images by combining the convolutional neural network (CNN) and fully convolutional network (FCN) methodology in serial. The fundamental finding is to detect and localize the tumor area with YOLO‐CNN and segment it with the FCN‐UNet architecture. This analysis provided automatic detection and segmentation as well as the location of the tumor. The segmentation using the UNet is run under four scenarios, and the best one is chosen by the minimum loss and maximum accuracy value. In this research, we used 277 images for training, 69 images for validation, and 14 images for testing. The validation is carried out by comparing the segmentation results with the medical ground truth to provide the correct classification ratio (CCR). This study succeeded in the detection of brain tumors and provided a clear area of the brain tumor with a high CCR of about 97%.

RTC_TongueNet: An improved tongue image segmentation model based on DeepLabV3.

Tongue segmentation as a basis for automated tongue recognition studies in Chinese medicine, which has defects such as network degradation and inability to obtain global features, which seriously affects the segmentation effect. This article proposes an improved model RTC_TongueNet based on DeepLabV3, which combines the improved residual structure and transformer and integrates the ECA (Efficient Channel Attention Module) attention mechanism of multiscale atrous convolution to improve the effect of tongue image segmentation. In this paper, we improve the backbone network based on DeepLabV3 by incorporating the transformer structure and an improved residual structure. The residual module is divided into two structures and uses different residual structures under different conditions to speed up the frequency of shallow information mapping to deep network, which can more effectively extract the underlying features of tongue image; introduces ECA attention mechanism after concat operation in ASPP (Atrous Spatial Pyramid Pooling) structure to strengthen information interaction and fusion, effectively extract local and global features, and enable the model to focus more on difficult-to-separate areas such as tongue edge, to obtain better segmentation effect. The RTC_TongueNet network model was compared with FCN (Fully Convolutional Networks), UNet, LRASPP (Lite Reduced ASPP), and DeepLabV3 models on two datasets. On the two datasets, the MIOU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy) values of the classic model DeepLabV3 were higher than those of FCN, UNet, and LRASPP models, and the performance was better. Compared with the DeepLabV3 model, the RTC_TongueNet network model increased MIOU value by 0.9% and MPA value by 0.3% on the first dataset; MIOU increased by 1.0% and MPA increased by 1.1% on the second dataset. RTC_TongueNet model performed best on both datasets. In this study, based on DeepLabV3, we apply the improved residual structure and transformer as a backbone to fully extract image features locally and globally. The ECA attention module is combined to enhance channel attention, strengthen useful information, and weaken the interference of useless information. RTC_TongueNet model can effectively segment tongue images. This study has practical application value and reference value for tongue image segmentation.

Improving Prediction Accuracy of Semantic Segmentation Methods Using Convolutional Autoencoder Based Pre-processing Layers

In this paper, we propose a method to improve prediction accuracy of semantic segmentation methods as follows: (1) construct a neural network that has pre-processing layers based on a convolutional autoencoder ahead of a semantic segmentation network, and (2) train the entire network initialized by the weights of the pre-trained autoencoder. We applied this method to the fully convolutional network (FCN) and experimentally compared its prediction accuracy on the cityscapes dataset. The Mean IoU of the proposed target model with the He normal initialization is 18.7% higher than that of FCN with the He normal initialization. In addition, those of the modified models of the target model are significantly higher than that of FCN with the He normal initialization. The accuracy and loss curves during the training showed that these are resulting from the improvement of the generalization ability. All of these results provide strong evidence that the proposed method is significantly effective in improving the prediction accuracy of FCN. The proposed method has the following features: it is comparatively simple, whereas the effect on improving the generalization ability and prediction accuracy of FCN is significant; the increase in the number of parameters by using it is very small, and that in the computation time is substantially large. In principle, the proposed method can be applied to other semantic segmentation methods. For semantic segmentation, at present, there is no effective way to improve the prediction accuracy of existing methods. None have published a method which is the same as or similar to our method and none have used such a method in practice. Therefore, we believe that our method is useful in practice and worthy of being widely known and used.

GLPanoDepth: Global-to-Local Panoramic Depth Estimation.

Depth estimation is a fundamental task in many vision applications. With the popularity of omnidirectional cameras, it becomes a new trend to tackle this problem in the spherical space. In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image. An omnidirectional image has a full field-of-view, providing much more complete descriptions of the scene than perspective images. However, fully-convolutional networks that most current solutions rely on fail to capture rich global contexts from the panorama. To address this issue and also the distortion of equirectangular projection in the panorama, we propose Cubemap Vision Transformers (CViT), a new transformer-based architecture that can model long-range dependencies and extract distortion-free global features from the panorama. We show that cubemap vision transformers have a global receptive field at every stage and can provide globally coherent predictions for spherical signals. As a general architecture, it removes any restriction that has been imposed on the panorama in many other monocular panoramic depth estimation methods. To preserve important local features, we further design a convolution-based branch in our pipeline (dubbed GLPanoDepth) and fuse global features from cubemap vision transformers at multiple scales. This global-to-local strategy allows us to fully exploit useful global and local features in the panorama, achieving state-of-the-art performance in panoramic depth estimation.

Identifying pests in precision agriculture using low-cost image data acquisition.

Unmanned Aerial Vehicles (UAVs), often called drones, have gained progressive prevalence for their swift operational ability as well as their extensive applicability in diverse real-world situations. Of late, UAV usage in precision agriculture has attracted much interest from scientific community. This study will look at drone aid in precise farming. Big data has the ability to analyze enormous amounts of data. Due to this, it is one of the diverse crucial technologies of Information and Communication Technology (ICT) which had applied in precision agriculture for the abstraction of critical information as well as for assisting agricultural practitioners in the comprehension of the most feasible farming practices, and also for better decision-making. This work analyses communication protocols, as well as their application toward the challenge of commanding a drone fleet for protecting crops from infestations of parasites. For computer-vision tasks as well as data-intensive applications, the method of deep learning has shown much potential. Due to its vast potential, it can also be used in the field of agriculture. This research will employ several schemes to assess the efficacy of models includes Visual Geometry Group (VGG-16), the Convolutional Neural Network (CNN) as well as the Fully-Convolutional Network (FCN) in plant disease detection. The methods of Artificial Immune Systems (AIS) can be used in order to adapt deep neural networks to the immediate situation. Simulated outcomes demonstrate that the proposed method is providing superior performance over various other technologically-advanced methods.

Digital Resource Development and Teaching Mode Innovation of Chinese Excellent Traditional Culture Calligraphy Art in Civic and Political Science Courses of Colleges and Universities

Abstract The excellent culture embedded in the art of calligraphy provides new teaching content for the design and realization of the educational objectives of the Civic and Political Science Program in colleges and universities and facilitates the formation of a new teaching mode. This paper combines the network calligraphy art resources, highlights the calligraphy interaction of the Civics course with the help of projection interaction settings, and proposes a new teaching mode of calligraphy art + Civics course in colleges and universities. It also proposes a full convolutional network algorithm based on an attention gate to deal with the task of binning calligraphic document images and extracting calligraphic resources. Combined with the design principles of interactive digital displays, the teaching concept and teaching design of the calligraphy art college Civics and Politics course are analyzed. Based on the results of convolutional neural network processing of calligraphy text image binarization and the recognition of calligraphy handwriting by convolutional neural network algorithm based on the Attention mechanism coding and decoding model, realize the practice of the new teaching mode of interactive calligraphy art + Civics and Politics course, and compare it with the traditional fusion method, and get the mean and standard deviation of the course grades of the traditional group and the interactive group as 36.12± respectively 6.35 points, 45.68 ± 4.73 points. The samples of different groups show significance (p<0.05) for all of participation, ability, challenge, and interest, which means that the mean value of the interactive group is higher than that of the traditional group in the four dimensions of participation, ability, challenge and interest in learning, which proves the feasibility of the teaching mode of the projected interactive calligraphy art resources + Civic and Political Science course proposed in this paper.

Multi-gas pollutant detection based on sparrow search algorithm optimized ALSTM-FCN.

It is critical to identify and detect hazardous, flammable, explosive, and poisonous gases in the realms of industrial production and medical diagnostics. To detect and categorize a range of common hazardous gasses, we propose an attention-based Long Short term memory Full Convolutional network (ALSTM-FCN) in this paper. We adjust the network parameters of ALSTM-FCN using the Sparrow search algorithm (SSA) based on this, by comparison, SSA outperforms Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Gray Wolf Optimization (GWO) Algorithm, Cuckoo Search (CS) Algorithm and other traditional optimization algorithms. We evaluate the model using University of California-Irvine (UCI) datasets and compare it with LSTM and FCN. The findings indicate that the ALSTM-FCN hybrid model has a better reliability test accuracy of 99.461% than both LSTM (89.471%) and FCN (96.083%). Furthermore, AdaBoost, logistic regression (LR), extra tree (ET), decision tree (DT), random forest (RF), K-nearest neighbor (KNN) and other models were trained. The suggested approach outperforms the conventional machine learning model in terms of gas categorization accuracy, according to experimental data. The findings indicate a potential for a broad range of polluting gas detection using the suggested ALSTM-FCN model, which is based on SSA optimization.

Buffer-text: Detecting arbitrary shaped text in natural scene image

Scene text detection has always been a research hotspot in computer vision and image understanding. With the development of deep learning, segmentation-based methods have achieved an exceptional effect in regular or curved text detection, but they cannot separate adjacent word-level texts. In this paper, we proposed a detector called Buffer-Text for the detection of irregular text in the natural scene image. First, the buffer region is proposed for bending text detection which widens the spatial distance between word-level texts. Then, a centerline-based polygon expansion algorithm is developed for the acquisition of the buffer region. After that, the scene text image is divided into different regions which are predicted by adopting the idea of multiclass semantic segmentation. To obtain effective segmentation results and solve the category imbalance problem, a Fully Convolutional Networks (FCN) with Spatial and Channel Squeeze & Excitation Block module is designed, and a loss function with adaptive weight updating is defined for the network. Ultimately, the post-processing including the total erosion and the single expansion is applied to eliminate the areas of noise in the segmented image and to separate the weak junctions in the word-level text. To verify the validity of the proposed method, several experiments were conducted on two curved text datasets, namely Total-Text and CTW1500, and the results indicated that the proposed method achieved significant accuracy in three statistical indicators (precision, recall, and F-score), particularly for the images with natural scenes and various text shapes.

Tool wear segmentation in blanking processes with fully convolutional networks based digital image processing

The extend of tool wear significantly affects blanking processes and has a decisive impact on product quality and productivity. For this reason, numerous scientists have addressed their research to wear monitoring systems in order to identify or even predict critical wear at an early stage. Existing approaches are mainly based on indirect monitoring using time series, which are used to detect critical wear states via thresholds or machine learning models. Nevertheless, differentiation between types of wear phenomena affecting the tool during blanking as well as quantification of worn surfaces is still limited in practice. While time series data provides partial insights into wear occurrence and evolution, direct monitoring techniques utilizing image data offer a more comprehensive perspective and increased robustness when dealing with varying process parameters. However, acquiring and processing this data in real-time is challenging. In particular, high dynamics combined with increasing strokes rates as well as the high dimensionality of image data have so far prevented the development of direct image-based monitoring systems. For this reason, this paper demonstrates how high-resolution images of tools at 600 spm can be captured and subsequently processed using semantic segmentation deep learning algorithms, more precisely Fully Convolutional Networks (FCN). 125,000 images of the tool are taken from successive strokes. Selected images are labeled pixel by pixel according to their wear condition and used to train a FCN (U-Net). The approach presented offers the possibility of spatially monitoring wear in high-speed blanking operations in real-time.

In-situ monitoring additive manufacturing process with AI edge computing

In additive manufacturing (AM), in-situ monitoring systems are vital for ensuring process quality. However, the widespread adoption of traditional high-speed camera-based monitoring systems is hindered by the prohibitively high cost of the required high-speed cameras. This paper introduces an innovative low-cost in-situ monitoring system that utilizes AI edge computing boards to expedite digital image processing without requiring high resolution (HR) video sequences. The system integrates a visual transformer-based video super resolution (ViTSR) network for reconstructing high resolution video frames and employs a fully convolutional network (FCN) to extract geometric characteristics of the molten pool and plasma arc simultaneously during AM processes. Comparing ViTSR with six state-of-the-art super-resolution methods, it achieved the highest peak signal-to-noise ratio (PSNR) of 38.16 dB on the test data. Moreover, the FCN utilized the reconstruction results from ViTSR, demonstrating an accuracy of 96.34% in multi-object extraction tasks. Through operator fusion and model pruning, the inference time of ViTSR and FCN on the AI edge board is optimized to 50.97 ms and 67.86 ms, respectively. Consequently, the proposed system achieves a total inference time of 118.83 ms, meeting the real-time quality monitoring needs of AM processes. Furthermore, this approach effectively reduces the expenses associated with high-speed camera-based monitoring systems, thereby promoting the widespread adoption of in-situ monitoring systems within the realm of AM.

A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation.

The morphological characteristics of a crack serve as crucial indicators for rating the condition of the concrete bridge components. Previous studies have predominantly employed deep learning techniques for pixel-level crack detection, while occasionally incorporating monocular devices to quantify the crack dimensions. However, the practical implementation of such methods with the assistance of robots or unmanned aerial vehicles (UAVs) is severely hindered due to their restrictions in frontal image acquisition at known distances. To explore a non-contact inspection approach with enhanced flexibility, efficiency and accuracy, a binocular stereo vision-based method incorporating full convolutional network (FCN) is proposed for detecting and measuring cracks. Firstly, our FCN leverages the benefits of the encoder-decoder architecture to enable precise crack segmentation while simultaneously emphasizing edge details at a rate of approximately four pictures per second in a database that is dominated by complex background cracks. The training results demonstrate a precision of 83.85%, a recall of 85.74% and an F1 score of 84.14%. Secondly, the utilization of binocular stereo vision improves the shooting flexibility and streamlines the image acquisition process. Furthermore, the introduction of a central projection scheme achieves reliable three-dimensional (3D) reconstruction of the crack morphology, effectively avoiding mismatches between the two views and providing more comprehensive dimensional depiction for cracks. An experimental test is also conducted on cracked concrete specimens, where the relative measurement error in crack width ranges from -3.9% to 36.0%, indicating the practical feasibility of our proposed method.

Fewer is more: efficient object detection in large aerial images

Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all patches, severely hindering the inference speed. This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results, enabling a simple and effective solution to object detection in large images. In brief, OAN is a light fully-convolutional network for judging whether each patch contains objects or not, which can be easily integrated into many object detectors and jointly trained with them end-to-end. We extensively evaluate our OAN with five advanced detectors. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets, meanwhile with consistent accuracy improvements. On extremely large Gaofen-2 images (29200$\times$27620 pixels), our OAN improves the detection speed by 70.5%. Moreover, we extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively, without sacrificing the accuracy. Code is available at https://github.com/Ranchosky/OAN.

Evaluating synthetic neuroimaging data augmentation for automatic brain tumour segmentation with a deep fully-convolutional network

Gliomas observed in medical images require expert neuro-radiologist evaluation for treatment planning and monitoring, motivating development of intelligent systems capable of automating aspects of tumour evaluation. Deep learning models for automatic image segmentation rely on the amount and quality of training data. In this study we developed a neuroimaging synthesis technique to augment data for training fully-convolutional networks (U-nets) to perform automatic glioma segmentation. We used StyleGAN2-ada to simultaneously generate fluid-attenuated inversion recovery (FLAIR) magnetic resonance images and corresponding glioma segmentation masks. Synthetic data were successively added to real training data (n = 2751) in fourteen rounds of 1000 and used to train U-nets that were evaluated on held-out validation (n = 590) and test sets (n = 588). U-nets were trained with and without geometric augmentation (translation, zoom and shear), and Dice coefficients were computed to evaluate segmentation performance. We also monitored the number of training iterations before stopping, total training time, and time per iteration to evaluate computational costs associated with training each U-net. Synthetic data augmentation yielded marginal improvements in Dice coefficients (validation set +0.0409, test set +0.0355), whereas geometric augmentation improved generalization (standard deviation between training, validation and test set performances of 0.01 with, and 0.04 without geometric augmentation). Based on the modest performance gains for automatic glioma segmentation we find it hard to justify the computational expense of developing a synthetic image generation pipeline. Future work may seek to optimize the efficiency of synthetic data generation for augmentation of neuroimaging data.

Performance Analysis of Artificial Intelligence Approaches for LEMP Classification

Lightning Electromagnetic Pulses, or LEMPs, propagate in the Earth–ionosphere waveguide and can be detected remotely by ground-based lightning electric field sensors. LEMPs produced by different types of lightning processes have different signatures. A single thunderstorm can produce thousands of LEMPs, which makes their classification virtually impossible to carry out manually. The lightning classification is important to distinguish the types of thunderstorms and to know their severity. Lightning type is also related to aerosol concentration and can reveal wildfires. Artificial Intelligence (AI) is a good approach to recognizing patterns and dealing with huge datasets. AI is the general denomination for different Machine Learning Algorithms (MLAs) including deep learning and others. The constant improvements in the AI field show us that most of the Lightning Location Systems (LLS) will soon incorporate those techniques to improve their performance in the lightning-type classification task. In this study, we assess the performance of different MLAs, including a SVM (Support Vector Machine), MLP (Multi-Layer Perceptron), FCN (Fully Convolutional Network), and Residual Neural Network (ResNet) in the task of LEMP classification. We also address different aspects of the dataset that can interfere with the classification problem, including data balance, noise level, and LEMP recorded length.

A three-stage pavement image crack detection framework with positive sample augmentation

Most pavement crack detection methods based on deep learning rely too much on pixel-wise labels, and are facing with sample imbalance problem. This paper proposes a three-stage pavement crack detection framework with positive sample augmentation by an autoencoder-deep convolutional generative adversarial network (E-DCGAN). Positive samples are the images labeled as crack in the training datasets. Firstly, crack regions are located using graph based on visual saliency (GBVS). Secondly, a convolutional neural network (CNN) model is designed to recognize crack pixels in the located crack regions. The E-DCGAN is explored to generate crack images, which are then added as augmented positive samples in the training dataset of the CNN model. The trained CNN model takes the located crack regions as inputs, outputs the recognized crack pixels. Finally, fine crack detection is realized through region growing. Crack contours are completely depicted pixel-wise based on the recognized crack pixels and the Breadth-first search. Experiments are carried out on two benchmark datasets and in practical applications. The results showed that the framework could detect cracks at the pixel level. Its performance is improved compared with standard fully convolutional network (FCN)-based and U-shaped CNN (U-Net)-based crack detection methods. In addition, the standard FCN-based and U-Net-based methods require accurate pixel-wise annotations, while the proposed framework only requires image-wise annotations. It can be concluded that the proposed framework not only effectively avoids the dependence on pixel-level annotations, improves the sample imbalance issues, but also achieves competitive detection performance over the FCN and U-Net based methods.

Automatic Plastic Greenhouse Extraction from Gaofen-2 Satellite Images with Fully Convolution Networks and Image Enhanced Index

Plastic greenhouses (PGs) play a vital role in modern agricultural development by providing a controlled environment for the cultivation of food crops. Their widespread adoption has the potential to revolutionize agriculture and impact the local environment. Accurate mapping and estimation of PG coverage are critical for strategic planning in agriculture. However, the challenge lies in the extraction of small and densely distributed PGs; this is often compounded by issues like irrelevant and redundant features and spectral confusion in high-resolution remote-sensing imagery, such as Gaofen-2 data. This paper proposes an innovative approach that combines the power of a full convolutional network (FC-DenseNet103) with an image enhancement index. The image enhancement index effectively accentuates the boundary features of PGs in Gaofen-2 satellite images, enhancing the unique spectral characteristics of PGs. FC-DenseNet103, known for its robust feature propagation and extensive feature reuse, complements this by addressing challenges related to feature fusion and misclassification at the boundaries of PGs and adjacent features. The results demonstrate the effectiveness of this approach. By incorporating the image enhancement index into the DenseNet103 model, the proposed method successfully eliminates issues related to the fusion and misclassification of PG boundaries and adjacent features. The proposed method, known as DenseNet103 (Index), excels in extracting the integrity of PGs, especially in cases involving small and densely packed plastic sheds. Moreover, it holds the potential for large-scale digital mapping of PG coverage. In conclusion, the proposed method providing a practical and versatile tool for a wide range of applications related to the monitoring and evaluation of PGs, which can help to improve the precision of agricultural management and quantitative environmental assessment.

Instance Segmentation Evaluation For Traffic Signs

This research paper focuses on developing a traffic sign recognition system based on the You Only Look At Coefficients (YOLACT) model, a one-stage instance segmentation model that offers high performance in terms of accuracy and reliability. However, the performance of YOLACT is influenced by various conditions such as day/night and different angles of objects. Therefore, this study aims to evaluate the impact of different angles and environments on the performance of the system. The paper discusses the framework, backbone structure, prototype generation branch, mask coefficient, and mask assembly used in the system. ResNet-101 and ResNet-50 were used as the backbone structure to extract feature maps of objects in the input image. The prototype generation branch generates prototype masks using fully convolutional networks (FCN), and the mask coefficient branch generates the Mask assembly using the sigmoid nonlinearity. Two models, YOLACT and Mask-RCNN, were evaluated in terms of mean precision (mAP) and frames per second (FPS) with the front view dataset. The results show that YOLACT outperforms Mask-RCNN in terms of accuracy and speed. For an image resolution of 550x550, YOLACT with Resnet-101 was considered the best model in this article since it achieves over 80% precision, recall, specificity, and accuracy in various conditions such as day, night, left and right, and forward-looking angles.