Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules

Li, Li; Fang, Baihao; Zhu, J.

doi:10.3390/app121910180

Cited by 15 publications

(7 citation statements)

References 41 publications

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“…The model improves the saliency of pavement damage by introducing a convolutional block attention module (CBAM) to suppress background noise and explores the influence of the embedding position of the CBAM module in the YOLOv4 model on detection accuracy. The results indicate that embedding CBAM into the neck and head modules can effectively improve the detection accuracy of the YOLOv4 model [26].…”

Section: U 2 -Net Network and Convolutional Block Attention Module (C...mentioning

confidence: 92%

Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation

Li,

Fan

et al. 2023

Applied Sciences

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Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and severe weather conditions affect image quality, which affects the accuracy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U2-Net network was constructed using multiscale inflation convolution and a multilayer convolutional block attention module (CBAM) attention mechanism for the U2-Net network. The experiments showed that (1) data augmentation solved the problem of an insufficient number of training samples to a certain extent and improved the accuracy of image segmentation; (2) this study designed a multilevel Gaussian noise data augmentation scheme to improve the network’s ability to resist noise interference and achieve a more accurate segmentation of images with different degrees of noise pollution; (3) the inclusion of a multiscale inflation perceptron and multilayer CBAM attention mechanism improved the ability of U2-Net network feature extraction and enhanced the model accuracy and generalization ability.

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Section: U 2 -Net Network and Convolutional Block Attention Module (C...mentioning

confidence: 92%

Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation

Li,

Fan

et al. 2023

Applied Sciences

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“…Most previous attention mechanisms for lightweight networks used SE modules [28,29], which only consider interchannel information but ignore positional information. Although later CBAM modules [30] tried to extract positional attention information by convolution after reducing the number of channels, convolution can only extract local relations and lacks the ability to extract long-range relations. Therefore, a new efficient attention mechanism, coordinate attention (CA), is proposed, which is able to encode horizontal and vertical location information into channel attention, allowing mobile networks to focus on a wide range of location information without incurring too much computational cost.…”

Section: Coordinate Attention Mechanismsmentioning

confidence: 99%

“…The model was built, trained, and validated using the Pytorch framework. The parameters of the YOLO model were initialized, and during the training process, the applied network model was designed with adaptive anchor frames, with initial anchor frame sizes set to [10,13,16,23,30,33], [30,45,59,61,62,119], and [90,116,156,198,326,373]. To perform network training, the learning rate was set to 0.01, and the number of training epochs was set to 200.…”

Section: Experimental Environmentmentioning

confidence: 99%

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

Meng,

Cui,

Liu

et al. 2023

Processes

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Under the background of intelligent manufacturing, in order to solve the complex problems of manual detection of metallurgical saw blade defects in enterprises, such as real-time detection, false detection, and the detection model being too large to deploy, a study on a metallurgical saw blade surface defect detection algorithm based on SC-YOLOv5 is proposed. Firstly, the SC network is built by integrating coordinate attention (CA) into the Shufflenet-V2 network, and the backbone network of YOLOv5 is replaced by the SC network to improve detection accuracy. Then, the SIOU loss function is used in the YOLOv5 prediction layer to solve the angle problem between the prediction frame and the real frame. Finally, in order to ensure both accuracy and speed, lightweight convolution (GSConv) is used to replace the ordinary convolution module. The experimental results show that the mAP@0.5 of the improved YOLOv5 model is 88.5%, and the parameter is 31.1M. Compared with the original YOLOv5 model, the calculation amount is reduced by 56.36%, and the map value is increased by 0.021. In addition, the overall performance of the improved SC-YOLOv5 model is better than that of the SSD and YOLOv3 target detection models. This method not only ensures the high detection rate of the model, but also significantly reduces the complexity of the model and the amount of parameter calculation. It meets the needs of deploying mobile terminals and provides an effective reference direction for applications in enterprises.

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“…Attention mechanisms were first proposed in the field of visual images and can be roughly divided into channel attention mechanisms, spatial attention mechanisms and mixed attention mechanisms. The attention mechanism is beneficial to improve the performance of the model and has made important breakthroughs in natural language processing and machine vision [19]. At present, the more commonly used attention mechanisms include ECA [20], CBAM [21], DANet [22], SENet [23], PSA [24], etc.…”

Section: Introductionmentioning

confidence: 99%

A study on 3D LiDAR-based point cloud object detection using an enhanced PointPillars network

Tao,

Su,

Zhang

et al. 2024

Meas. Sci. Technol.

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The PointPillar target detection algorithm is a mainstream 3D lidar point cloud target detection algorithm that has a fast response speed but low detection accuracy. Addressing the problem of the low detection accuracy of the PointPillar target detection network, we propose an improved PointPillar target detection algorithm that integrates an attention mechanism. The algorithm first introduces the attention mechanism and strengthens the feature extraction module based on PointPillar to realize the amplification of the local information in the three scale feature maps and to better extract the more important feature information. Then, our algorithm adds an anchor free type detector head to further optimize the detector head module. The experimental results show that the optimized PointPillar target detection algorithm has achieved good test results in the KITTI data set. Under medium difficulty, the AOS mode mAP reaches 79.76%, the 3D mode mAP reaches 82.03%, and the BEV mode mAP reaches 82.30%. Compared with that of other point cloud target detection algorithms, the detection accuracy of our algorithm is improved by approximately 10%.

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Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules

Cited by 15 publications

References 41 publications

Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation

Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

A study on 3D LiDAR-based point cloud object detection using an enhanced PointPillars network

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