Abstract:

For small-scale targets and domain transfer problems, a method based on a continuous unsupervised domain adaptation strategy is proposed. By removing low-resolution feature maps and enhancing high-resolution feature maps, the method improves the ability of small-scale floaters to extract features. This study proposes a continuous unsupervised domain adaptation method that integrates unsupervised domain adaptation, buffering, and sample replay to reduce the constantly varying domain transfer variance in application scenarios. Meanwhile, this study combines the improved detection network with continual unsupervised domain adaption to improve model detection precision and generalization capabilities. Through the experimental verification on the data set of the floating targets, compared with the mainstream methods, the detection accuracy of the proposed method reaches 82.2%, the detection speed can reach 68.5 f/s, the computation amount of floating-point numbers reaches 3.3 billion, and the size of the model reaches 25.3 MB. This study extends the application of object detection in water surface vision.

Key words: deep learning, floating materials, object detection, unsupervised domain adaptation, continual learning

4类场景定量化分析结果"

方法	渔船	水葫芦	漂浮杂草	塑料瓶	mAP/%
Data1→Data2
SSD-FT	63.2	72.3	65.8	66.3	66.9
CM-SSD	73.9	78.2	76.2	69.2	74.4
DA-FRCNN	68.1	77.4	74.1	73.8	73.4
D-adapt	79.8	80.3	76.3	76.4	78.2
CDA-SSD-FT	83.9	87.8	85.6	84.2	85.4
Data2→Data3
SSD-FT	58.3	67.5	66.8	56.3	62.2
CM-SSD	52.2	65.2	64.2	60.2	60.5
DA-FRCNN	63.5	68.3	70.4	66.6	67.2
D-adapt	59.4	72.5	72.5	72.4	69.2
CDA-SSD-FT	79.2	78.4	82.8	79.1	79.9
Data3→Data4
SSD-FT	70.2	72.5	68.3	62.5	68.4
CM-SSD	72.4	68.3	70.4	66.3	69.4
DA-FRCNN	76.9	77.2	73.9	70.5	74.6
D-adapt	71.7	75.1	76.7	72.6	74.0
CDA-SSD-FT	84.9	83.1	81.3	79.4	82.2
Data4→Data5
SSD-FT	72.3	69.2	73.5	69.3	71.0
CM-SSD	68.4	70.3	72.7	71.6	70.8
DA-FRCNN	70.2	73.6	77.5	74.7	74.0
D-adapt	74.9	71.7	74.2	75.9	74.2
CDA-SSD-FT	82.5	79.4	81.3	81.0	81.1

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