Xu Chen, Dongliang Peng, Yu Gu. Real-time object detection for UAV images based on improved YOLOv5s[J]. Opto-Electronic Engineering, 2022, 49(3): 210372-1
- Opto-Electronic Engineering
- Vol. 49, Issue 3, 210372-1 (2022)
Fig. 1. YOLOv5 backbone network architecture diagram
Fig. 2. Structure diagram of feature fusion module
Fig. 3. (a) Res-DConv module; (b) Receptive field mapping
Fig. 4. Improved module structure
Fig. 5. YOLOv5sm+ model architecture
Fig. 6. (a) Total number of category instances on the VisDrone dataset; (b) Classes confusion matrix of YOLOv5m algorithm
Fig. 7. The detection examples of different algorithms in the VisDrone UAV scene. (a) YOLOv5m model; (b) YOLOv5sm+ model; (c) YOLOv5s model
Fig. 8. Comparison of the detection effects of three algorithms in dense vehicle scenes. (a) YOLOv5m; (b) YOLOv5s; (c) YOLOv5sm+
Fig. 9. Detection comparison of improved algorithm in DIOR dataset. (a) YOLOv5s; (b) YOLOv5sm+
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Table 1. Receptive field analysis table
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Table 2. Pre-setting anchors in response to the receptive field and down-sampling
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Table 3. Statistics of different types of objects
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Table 4. Performance comparison experiment results of depth and width models
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Table 5. Verification experiment results on Res-Dconv module
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Table 6. The ablation experiment results of our algorithm modules on the VisDrone dataset
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Table 7. Detection performance of different algorithms on VisDrone dataset
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Table 8. Detection performance of different algorithms on DIOR dataset
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