Lightweight Model for Irregular Wear Detection in Power Operations

Fig. 6. Model parameter quantity distribution before and after improvement. (a) YOLOv8n parameter quantity distribution; (b) YOLO-WWSP parameter quantity distribution

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Fig. 7. Comparison of YOLOv8n (left) and YOLO-WWSP (right) test results

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Table 1. Experimental model parameters
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Table 1. Experimental model parameters
Training parameter Value
Learning rate 0.01
Batch size 32
Epochs 200
Image size 640×640
Momentum 0.937
Weight decay 0.0005

Table 2. Results of the ablation experiments

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Table 2. Results of the ablation experiments

Model	SCTADH	DStar	MLFCA	LAMP	mAP@0.5 /%	mAP@0.5∶0.95 /%	Params	FLOPs /10⁹	Model size /MB
1					91.1	74.3	3151904	8.7	5.95
2	√				91.3	76.1	2245235	8.7	4.48
3		√			91.5	75.3	2849152	7.8	5.38
4	√	√			91.0	75.9	2124659	8.1	4.26
5	√	√	√		92.5	76.2	2125995	8.1	4.28
6	√	√	√	√	91.8	75.8	953402	5.3	2.05

Table 3. Comparison of the five attention mechanisms
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Table 3. Comparison of the five attention mechanisms
Model mAP@0.5 /% mAP@0.5∶0.95 /%
4 91.0 75.9
+CBAM 92.4 75.9
+ELA 92.1 76.1
+EMA 92.2 75.3
+LSKA 91.7 75.9
+MLFCA 92.5 76.2

Table 4. Comparison of the experimental results

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Table 4. Comparison of the experimental results

Model	mAP@0.5 /%	mAP@0.5∶0.95 /%	Params	FLOPs /10⁹	Model size /MB
RTDETR-L	87.0	72.3	28607660	101.0	56.33
YOLOv3n	88.9	70.3	12168784	19.0	23.25
YOLOv5n	89.7	73.4	2649200	7.7	5.02
YOLOv6n	91.4	75.2	4495392	13.0	8.28
YOLOv8n	91.1	74.3	3151904	8.7	5.95
YOLOv10n	91.1	72.9	2299264	6.7	5.50
NanoDet-m	88.4	67.9	910800	1.3	3.78
NanoDet-m-1.5×	89.0	69.4	2010640	2.7	8.05
NanoDet-plus-m-1.5×	89.7	70.3	2404496	3.4	29.99
NanoDet-g	84.6	62.4	3753184	4.9	14.99
PicoDet-xs	83.9	57.2	689562	1.3	2.67
PicoDet-s	84.7	60.5	1176184	1.9	4.53
PicoDet-m	87.2	63.5	3461216	5.1	13.25
YOLO-WWSP	91.8	75.8	953402	5.3	2.05

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Guangle Wang, Yatong Zhou, Zhao Wang. Lightweight Model for Irregular Wear Detection in Power Operations[J]. Laser & Optoelectronics Progress, 2025, 62(6): 0615007

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