A quantitative detection algorithm based on improved faster R-CNN for marine benthos

In order to realize the accurate quantitative detection of marine benthos and solve the problems in detecting small and densely distributed benthic organisms under overlapping and occlusion image, a quantitative detection algorithm for marine benthos based on Faster R-CNN is proposed. A convolution kernel adaptive selection unit is embedded in the backbone to enhance the feature extraction ability of network. Based on this, multi-resolution feature fusion is introduced to design deconvolution feature pyramid structure for small object detection. At the same time, the selection of anchor in Region Proposal Network is optimized to improve the accuracy of counting. Transfer learning strategy is also employed to train the proposed model and alleviate the limitation of small dataset. The results show that compared with the original Faster R-CNN, the proposed algorithm improves the recognition precision of marine benthos from 93.25% to 96.32%, and reduces the mean average error from 16.53 to 7.38. This improvement reflects that the proposed algorithm is more suitable for the quantitative detection of small and dense objects on the seafloor.

Automated summary

This study proposed a quantitative detection algorithm for marine benthos based on Faster R-CNN, which improved the recognition precision of benthic organisms by optimizing the network structure and employing various techniques, suitable for quantitative detection of small and dense objects on the seafloor.