Zunliang Chen1,2, Chengxu Huang1,2, Lucheng Duan1,2, Baohua Tan1,2,*
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1085-1102, 2023, DOI:10.32604/cmc.2023.039451
Abstract In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower, a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to provide core technical support for real-time water surface litter detection by water surface litter cleanup vessels. The method reduces network parameters by introducing the deep separable convolution GhostConv in the lightweight network GhostNet to substitute the ordinary convolution in the original YOLOv5s feature extraction and fusion network; introducing the C3Ghost module to substitute the C3 module in the original backbone and neck networks to further reduce… More >
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