Underwater Image Object Detection Based on AWAF-YOLO Algorithm

Table 1. Comparative experimental results of loss function optimization
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Table 1. Comparative experimental results of loss function optimization
Model P R mAP@0.50 mAP@0.50∶0.95
YOLOv8n 96.2 95.6 98.5 83.8
YOLOv8n+Focaler-IoU 96.1 95.9 98.5 84.0
YOLOv8n+Shape-IoU 95.7 96.4 98.6 84.0
YOLOv8n+FSIoU 95.9 96.4 98.6 84.3
AWAF-YOLO 96.7 96.9 98.8 86.4

Table 2. Ablation experimental results on the TrashCan1.0 dataset

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Table 2. Ablation experimental results on the TrashCan1.0 dataset

ADown	C2f-WTConv	SPPFAC	FSIoU	P /%	R /%	mAP@0.50 /%	mAP@0.50∶0.95 /%	Parameters /10⁶	FLOPS /10⁹
×	×	×	×	96.2	95.6	98.5	83.8	3.0	8.2
√	×	×	×	95.7	96.2	98.5	84.0	2.6	7.3
×	√	×	×	95.9	95.9	98.5	83.6	2.6	7.2
×	×	√	×	95.7	97.0	98.7	84.8	3.8	8.9
×	×	×	√	95.8	96.4	98.6	84.3	3.0	8.2
√	√	×	×	96.0	96.3	98.5	84.4	2.5	10.9
√	√	√	×	96.5	96.8	98.7	86.2	3.4	11.6
√	√	√	√	96.7	96.9	98.8	86.4	3.4	11.6

Table 3. Ablation experimental results on the Aquarium dataset

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Table 3. Ablation experimental results on the Aquarium dataset

ADown	C2f-WTConv	SPPFAC	FSIoU	P /%	R /%	mAP@0.50 /%	mAP@ 0.50∶0.95 /%	Parameters /10⁶	FLOPS /10⁹
×	×	×	×	97.7	95.4	97.8	81.9	3.0	8.2
√	×	×	×	97.2	96.0	97.9	82.2	2.6	7.3
×	√	×	×	97.4	95.6	97.7	81.7	2.6	7.2
×	×	√	×	97.8	95.7	97.9	83.1	3.8	8.9
×	×	×	√	97.4	95.4	97.9	82.2	3.0	8.2
√	√	×	×	97.4	95.8	97.8	82.1	2.5	10.9
√	√	√	×	98.0	95.7	98.0	83.4	3.4	11.6
√	√	√	√	97.8	96.1	98.1	83.5	3.4	11.6

Table 4. Comparative experimental results of different models

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Table 4. Comparative experimental results of different models

Model	P /%	R /%	mAP@0.50 /%	mAP@0.50∶0.95 /%	Parameters /10⁶	FLOPS /10⁹
YOLOv3-tiny	93.9	96.2	98.5	78.7	8.7	13.1
YOLOv5s	96.3	98.2	98.8	84.4	7.1	16.1
YOLOv6s	94.3	93.8	97.0	81.2	4.2	11.9
YOLOv9s	81.5	86.5	90.7	70.2	9.7	39.7
YOLOv8n	96.2	95.6	98.5	83.8	3.0	8.2
AWAF-YOLO	96.7	96.9	98.8	86.4	3.4	11.6

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Zhenghu Zhu, Zhen Su, Wei Wang. Underwater Image Object Detection Based on AWAF-YOLO Algorithm[J]. Laser & Optoelectronics Progress, 2025, 62(16): 1601001

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