Multi-Scale Underwater Small Object Detection Based on Multi-Rate Dilated Convolution

Abstract

Owing to the complex imaging of underwater scenes，lower resolution，and insufficient information about small objects，extracting effective feature information is difficult，resulting in a low recognition rate and high false alarm rate for small underwater objects.To solve this problem，this paper proposes a multi-scale underwater small object detection method based on multi-rate dilated convolution.First，the DarkNet53 backbone model is used for feature extraction to obtain high-level semantic information，and a multi-rate dilated convolution module is adopted to expand the receptive field of the network，obtaining feature information in a larger pixel range by adjusting the dilated rates. Additionally，a residual structure is added to ensure detailed information on small objects for positioning.Subsequently， to restore the resolution of the small object，a deconvolution module is used to reconstruct the image details，and the detailed features are learned from feature maps with different resolutions.Finally，through Feature Pyramid Network（FPN），richer multi-scale context information is introduced into the deconvolution layer such that multiple levels of features are learned across scales to enhance the positioning and classification of small objects.Additionally，feature integration and screening are performed on the output of each layer after feature fusion to obtain the final prediction results. Experimental results show that the method achieves mAP values of 82.6% and 81.5% on the two public datasets of Pascal VOC2007 and URPC2018，respectively，and the speeds are 34.4 and 34.2 frame/s，respectively.This can effectively enhance the ability to detect small underwater objects in real time.