Strong Interference Target Detection on the Sea Surface Based on Feature Augmentation

Fig. 6. Comparison of detection results and heatmaps on the AFO test set by baseline model and proposed model. (a) Input images; (b)‒(c) detection results; (d)‒(e) heatmaps

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Fig. 7. Comparison of detection results and heatmaps on the SeaDronesSee test set by baseline model and proposed model. (a) Input images; (b)‒(c) detection results; (d)‒(e) heatmaps

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Table 1. Configuration and parameters setting for experiment
View table
Table 1. Configuration and parameters setting for experiment
Parameter Configuration
Python version 3.11
PyTorch version 2.0.0
CUDA version 11.7
Learning rate 0.01
Optimizer Stochastic gradient descent （SGD）
Batch size 16
Epoch 100
Momentum 0.937
Weight decay 5×10^-4

Table 2. Overall performance of different algorithms on the AFO dataset

View table

Table 2. Overall performance of different algorithms on the AFO dataset

Algorithm	Parameters /10⁶	GFLOPs	P /%	R /%	mAP₅₀/%	mAP₉₅ /%	FPS /（frame·s^-1）
Faster RCNN	136.79	369.84	42.96	47.97	41.01	17.39	17
SSD	24.28	61.25	72.26	19.96	44.10	22.26	39
YOLOv3	61.55	65.63	83.70	46.12	53.99	20.69	35
YOLOv4	63.97	59.99	68.28	20.82	35.79	13.80	62
YOLOv5	46.17	108.31	94.01	51.01	57.83	31.69	73
YOLOX	54.15	155.69	94.31	75.26	76.26	46.10	69
YOLOv7	37.22	105.20	87.40	76.76	84.88	44.80	81
RetinaNet	36.43	146.91	77.69	28.06	35.24	19.36	71
CenterNet	32.67	70.22	90.89	52.02	61.71	28.89	82
RT-DETR	42.31	136.74	89.45	83.79	88.09	47.68	108
YOLOv8	11.64	28.43	90.1	84.69	89.54	53.67	114
TS-YOLOv8	12.17	29.06	94.54	91.56	95.14	61.05	110

Table 3. AP of each category in the AFO dataset detected by different algorithms

View table

Table 3. AP of each category in the AFO dataset detected by different algorithms

Algorithm	AP₅₀						mAP₅₀
Algorithm	Human	Board	Boat	Buoy	Sailboat	Kayak	mAP₅₀
Faster RCNN	19.26	69.32	45.78	—	34.01	77.69	41.01
SSD	22.12	87.83	31.83	10.06	34.13	78.63	44.10
YOLOv3	62.11	87.92	30.34	30.08	31.11	82.40	53.99
YOLOv4	32.49	78.54	12.90	—	28.70	62.13	35.79
YOLOv5	82.04	97.90	46.53	16.65	15.80	88.05	57.83
YOLOX	91.43	98.88	58.10	86.20	34.69	88.23	76.26
YOLOv7	82.72	98.72	68.40	79.34	80.88	99.24	84.88
RetinaNet	4.29	74.36	37.72	—	33.79	61.26	35.24
CenterNet	65.54	82.38	55.02	45.85	33.56	87.93	61.71
RT-DETR	84.78	98.26	77.51	84.18	84.92	98.79	88.07
YOLOv8	82.75	98.19	80.64	88.18	87.92	99.54	89.54
TS-YOLOv8	89.82	99.14	91.54	94.48	96.28	99.60	95.14

Table 4. Comparison of different attention mechanisms

View table

Table 4. Comparison of different attention mechanisms

Model	Parameters /10⁶	GFLOPs	P /%	R /%	mAP₅₀ /%	mAP₉₅ /%
YOLOv8	11.64	28.43	90.10	84.69	89.54	53.67
YOLOv8+EMA	11.64	28.40	91.07	85.79	89.75	53.69
YOLOv8+SimAM	11.64	28.43	91.29	85.30	90.06	55.01
YOLOv8+CA	11.75	28.91	90.95	85.90	90.18	54.92
YOLOv8+GAM	11.71	28.77	90.10	84.97	89.66	53.70
YOLOv8+CBAM	11.64	28.53	92.10	86.36	91.09	55.15
YOLOv8+SA	11.64	28.43	91.91	87.02	91.45	55.74

Table 5. Overall performance of each ablation algorithm on the AFO dataset

View table

Table 5. Overall performance of each ablation algorithm on the AFO dataset

YOLOv8	TFF	SA	Parameters /10⁶	GFLOPs	P /%	R /%	mAP₅₀ /%	mAP₉₅ /%	FPS /（frame·s^-1）
√			11.64	28.43	90.10	84.69	89.54	53.67	114
√	√		12.17	29.06	92.35	88.61	93.46	59.79	112
√		√	11.64	28.43	91.91	87.02	91.45	55.74	112
√	√	√	12.17	29.06	94.54	91.56	95.14	61.05	110

Table 6. AP of each category in AFO dataset detected by each ablation algorithm

View table

Table 6. AP of each category in AFO dataset detected by each ablation algorithm

YOLOv8	TFF	SA	AP₅₀						mAP₅₀
YOLOv8	TFF	SA	Human	Board	Boat	Buoy	Sailboat	Kayak	mAP₅₀
√			82.75	99.19	80.64	78.18	90.92	99.54	89.54
√	√		88.27	99.29	92.52	86.60	94.49	99.56	93.46
√		√	87.09	98.48	87.27	84.35	91.98	99.53	91.45
√	√	√	89.82	99.14	91.54	94.48	96.28	99.60	95.14

Table 7. Overall performance of each ablation algorithm on the SeaDronesSee dataset

View table

Table 7. Overall performance of each ablation algorithm on the SeaDronesSee dataset

YOLOv8	TFF	SA	Parameters /10⁶	GFLOPs	P /%	R /%	mAP₅₀/%	mAP₉₅/%	FPS /（frame·s^-1）
√			11.64	28.43	88.42	82.82	86.87	57.61	110
√	√		12.17	29.06	90.37	86.14	90.09	61.91	107
√		√	11.64	28.43	88.93	85.75	88.71	59.47	108
√	√	√	12.17	29.06	91.66	87.59	91.34	63.53	106

Table 8. AP of each category in SeaDronesSee dataset detected by each ablation algorithm
View table
Table 8. AP of each category in SeaDronesSee dataset detected by each ablation algorithm
YOLOv8 TFF SA AP₅₀ mAP₅₀
Swimmer Boat Jetski Life jacket Buoy
√ 80.15 92.63 90.76 81.01 89.80 86.87
√ √ 85.75 94.13 92.93 85.08 92.56 90.09
√ √ 83.99 93.49 91.86 83.05 91.16 88.71
√ √ √ 87.04 95.41 94.60 86.38 93.27 91.34

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Xiang Long, Huajie Chen, Haoyu Wu, Di Yu. Strong Interference Target Detection on the Sea Surface Based on Feature Augmentation[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0212008

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Paper Information

Category: Instrumentation, Measurement and Metrology

Received: Apr. 23, 2024

Accepted: Jun. 11, 2024

Published Online: Dec. 17, 2024

The Author Email:

DOI:10.3788/LOP241156

CSTR:32186.14.LOP241156

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Configuration and parameters setting for experiment

Table 1. Configuration and parameters setting for experiment

Table 2. Overall performance of different algorithms on the AFO dataset

Table 2. Overall performance of different algorithms on the AFO dataset

Table 3. AP of each category in the AFO dataset detected by different algorithms

Table 3. AP of each category in the AFO dataset detected by different algorithms

Table 4. Comparison of different attention mechanisms

Table 4. Comparison of different attention mechanisms

Table 5. Overall performance of each ablation algorithm on the AFO dataset

Table 5. Overall performance of each ablation algorithm on the AFO dataset

Table 6. AP of each category in AFO dataset detected by each ablation algorithm

Table 6. AP of each category in AFO dataset detected by each ablation algorithm

Table 7. Overall performance of each ablation algorithm on the SeaDronesSee dataset

Table 7. Overall performance of each ablation algorithm on the SeaDronesSee dataset

Table 8. AP of each category in SeaDronesSee dataset detected by each ablation algorithm

Table 8. AP of each category in SeaDronesSee dataset detected by each ablation algorithm