An improved YOLOv5 transmission line bird nest detection method was proposed, in order to improve the detection performance and accuracy of the occluded bird nest targets, as well as reduce the threat of bird nesting to the stable operation of the power system and the operation and maintenance cost. Firstly, the asymptotic feature pyramid network was used to optimize the original feature pyramid network structure, effectively avoiding the large semantic gap between non-adjacent layers, and enhancing the fusion effect between non-adjacent layers. Secondly, the multi-scale dilated attention mechanism was used to enable the model to effectively extract semantic information at different scales and improve the detection performance of the model for occluded bird nest targets. Finally, the lightweight MobileNetV3 network was adopted as the backbone network to further reduce the complexity of the model. Ablation experiments and qualitative experimental analysis demonstrated that, the recall, precision and mean average precision of the improved algorithm were respectively improved by 2.0 percentage point, 0.7 percentage point and 1.7 percentage point compared with the original algorithm, and the weight and the computational amount were reduced by 74.7 percentage point and 53.5 percentage point, respectively. The results showed good performance for the occluded bird nest targets, which verified the effectiveness of the improved method.
Tab.1Results of lightweight network comparison experiment
Fig.3Comparison experiment results of mean average precision of lightweight network
Fig.4Depth separable convolution
Fig.5Standard convolution
Fig.6SE channel attention mechanism
Fig.7Multi-scale dilated attention
Fig.8Visualization comparison of attention map
Fig.9AFPN asymptotic architecture
Fig.10Loss function curve for improved and original algorithms
Fig.11Performance comparison for improved and original algorithms
Fig.12Comparison of mean average precision
实验
MobileNet-V3 轻量化网络
AFPN
MSDA
P/%
R/%
mAP@0.5/%
S/MB
F/109
YOLOv5
—
—
—
89.6
94.0
89.9
34.4
15.9
1
√
—
—
89.1
93.0
89.6
25.8
11.3
2
—
√
—
92.8
91.0
88.8
29.7
20.4
3
—
—
√
91.9
93.0
89.7
31.5
16.8
4
√
√
—
87.3
96.0
91.4
9.2
7.9
5
√
—
√
87.1
94.0
89.0
5.7
2.5
6
—
√
√
91.8
91.0
89.4
33.7
21.3
改进算法
√
√
√
90.3
96.0
91.6
8.7
7.4
Tab.2Results of ablation experiment
实验
P/%
R/%
mAP@0.5/%
S/MB
F/109
Faster R-CNN
77.8
87.5
86.3
108.2
251.4
SSD
83.3
75.0
79.7
90.6
72.3
YOLOv3
94.8
91.0
89.3
235.0
155.3
YOLOv4
88.6
79.9
86.1
244.4
100.7
YOLOv5
89.6
94.0
89.9
34.4
15.9
改进算法
90.3
96.0
91.6
8.7
7.4
Tab.3Comparison of results of different algorithms
Fig.13Illustration of occluded test image
Fig.14Detection results of different algorithm
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