Class-incremental update method for target recognition models in sidescan sonar images

doi:10.11947/j.AGCS.2026.20250362

Abstract

Abstract:

The inter-class similarity of side-scan sonar (SSS) target images can induce feature bias during class-incremental updates of recognition models, exacerbating catastrophic forgetting. To address this issue, this paper proposes a stage-progressive dual-scale dynamic attention module based on MEMO algorithm, which composes of intra-stage and inter-stage attention. The intra-stage attention employs global pooling and channel reweighting on stage-specific block features to enhance the model's representational capability and alleviate confusion among similar categories within the same stage. The inter-stage attention reweights the concatenated features based on the cross-stage information to mitigate feature bias dominated by new categories. Combined with a nearest-neighbor classifier, the overall approach further strengthens the model's anti-forgetting capability. Under the proposed class-incremental model update framework for SSS object recognition, our method achieves an average accuracy (Avg) of 86.79% and a last-stage accuracy of 80.94%, surpassing the baseline by 10.88 and 11.43 percentage, respectively, and outperforming mainstream class-incremental learning methods. Extended experiments on a large-scale open-source forward-looking sonar dataset further demonstrate the method's generalization ability, yielding an Avg improvement of 2.65 percentage. Our method introduces only 1.41% additional parameters while maintaining lightweight update overhead and efficient inference speed. Experimental results show that our method effectively suppresses feature interference caused by inter-class similarity, improving stability and robustness during dynamic category expansion. This provides an efficient solution for continual recognition of underwater sonar targets and holds significant value for mobile deployment and intelligent, unmanned underwater mapping tasks.

Key words: underwater target recognition, model class-incremental update, dynamic attention, inter-class similarity, side-scan sonar images

CLC Number:

P209

Yongcan YU, Jianhu ZHAO, Bingmo LI, Ziyang HE. Class-incremental update method for target recognition models in sidescan sonar images[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(3): 502-514.

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References 36

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序号	类别	训练集	测试集	总计
0	沉船	500（350）	150	650（500）
1	人工鱼礁	500（350）	150	650（500）
2	管道	500（283）	122	622（405）
3	人体	500（231）	100	600（331）
4	失事飞机	500（102）	45	545（147）
5	集装箱	500（67）	30	530（97）

方法	类别零散程度	Last/（%）	Avg/（%）
MEMO	222	64.54±5.41	77.32±4.97
	2121	69.79±3.38	72.67±5.04
	3111	74.2±2.32	77.74±1.94
	平均	69.51±5.56	75.91±4.82
本文方法	222	81.46±1.3	88.64±0.84
	2121	78.78±2.79	83.82±2.81
	3111	82.58±3.56	87.91±1.52
	平均	80.94±3.15	86.79±2.85

组合	动态注意力	最近邻分类器	Last/（%）	Avg/（%）
组合1			69.51±5.56	75.91±4.82
组合2	√		77.24±4.60	82.77±4.31
组合3		√	73.18±2.73	81.15±2.56
组合4	√	√	80.94±3.15	86.79±2.85

增量方法	Last/（%）	Avg/（%）	参数量	推理速度/FPS	总训练时长/min
iCaRL	61.86	75.64	11 178 564	16 542	76.52
PODNet	62.87	78.91	11 196 993	15 375	78.10
DER	56.45	72.19	33 540 297	5843	77.17
MEMO	65.83	78.96	27 974 729	10 492	76.09
BEEF	66.94	77.28	33 541 848	1447	122.56
FECIL	76.27	85.45	11 170 884	27 361	457.35
MEMO+动态注意力	75.15	83.33	28 367 945	9238	76.58
MEMO+最近邻分类器	74.09	84.17	27 974 729	9429	76.09
本文方法	81.46	88.64	28 367 945	8192	76.58

方法	类别零散程度	Last/（%）	Avg/（%）
MEMO	333 333	93.01	90.94
	52 416	94.62	95.38
	4 223 142	92.94	94.92
	平均	93.52	93.75
本文方法	333 333	94.96	95.24
	52 416	95.16	97.22
	4 223 142	92.81	96.73
	平均	94.31	96.40