An Image Object Detection Model Based on Mixed Attention Mechanism Optimized YOLOv5
Abstract
:1. Introduction
- In this paper, the YOLOv5 feature extraction network is improved, and the feature map extraction operation is advanced. If it is a small object, the deeper the network level is, the less semantic information is retained by the small object. By extracting the feature map in advance, more abundant location information can be obtained to improve the problem of the feature loss of object information as the network level deepens;
- The network model proposed in this paper adds a selfattention mechanism. The attention mechanism can improve the object feature weight, reduce the background feature weight, let the model obtain the object area that needs to be focused on, and reconstruct the features;
- The network model proposed in this paper uses a context feature-fusion structure. The feature information with rich deep semantics but also unclear location information is fused to the feature information with clear shallow location information but not rich semantics to improve the detection difficulty caused by complex multiobject;
- The remainder of this paper is organized as follows: Section 2 discusses work related to image object detection, followed, in Section 3, by the object detection algorithms based on deep learning. In Section 4, the proposed method is addressed along with the considerations for the analysis of this paper. Section 5 presents the experimental results and analysis. Finally, in Section 6, a conclusion is drawn.
2. Related Work
3. Object Detection Algorithms Based on Deep Learning
3.1. Classification of Mainstream Algorithms in Object Detection
3.2. SSD Object Detection Algorithm
3.2.1. SSD Algorithm Model Structure
3.2.2. Prior Frame
3.2.3. Border Regression
3.2.4. Loss Function of SSD
3.3. YOLOv3 Object Detection Algorithm
3.3.1. YOLOv3 Network Structure
3.3.2. Residual Module
3.3.3. Loss Function of YOLOv3
4. Image Object Detection Based on YOLOv5 Optimized by Mixed Attention Mechanism
4.1. Attention Mechanism
4.1.1. Channel Attention Mechanism
4.1.2. Spatial Attention Mechanism
4.1.3. Mixed Attention Mechanism
4.1.4. Channel Attention Module CAM
4.1.5. Spatial Attention Module SAM
4.2. Construction of MAO-YOLOv5 Model Feature Extraction Structure
4.3. Modified Loss Function
5. Experimental Process and Result Analysis
5.1. Experiment Configuration and Dataset
5.2. Evaluating Indicators
- (1)
- P-R curve
- (2)
- mAP
5.3. Experimental Process and Result Analysis
6. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Method | mAP | FPS |
---|---|---|
YOLOv3 | 82.36% | 16.74 |
YOLOv5 | 84.75% | 34.69 |
SSD | 86.64% | 18.99 |
MAO-YOLOv5 | 92.30% | 23.07 |
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Sun, G.; Wang, S.; Xie, J. An Image Object Detection Model Based on Mixed Attention Mechanism Optimized YOLOv5. Electronics 2023, 12, 1515. https://doi.org/10.3390/electronics12071515
Sun G, Wang S, Xie J. An Image Object Detection Model Based on Mixed Attention Mechanism Optimized YOLOv5. Electronics. 2023; 12(7):1515. https://doi.org/10.3390/electronics12071515
Chicago/Turabian StyleSun, Guangming, Shuo Wang, and Jiangjian Xie. 2023. "An Image Object Detection Model Based on Mixed Attention Mechanism Optimized YOLOv5" Electronics 12, no. 7: 1515. https://doi.org/10.3390/electronics12071515