Underwater Object Detection Based on Spatial Pyramid and Channel Attention

Abstract

In order to solve the problems of low accuracy and high detection delay of conventional object detector in underwater environment, underwater object detection network model based on single-stage target detection is introduced. The specific work is as follows: In order to solve the problem that conventional detectors have low detection accuracy in detecting blurry small targets in underwater scenes due to water quality, a channel spatial attention mechanism is designed, which enables the model to focus more on the feature learning of target objects. This improves the extraction of information within the channel and enhancing the extraction of salient features in cases where the distinction between the foreground and background is not obvious and improves the accuracy on small target in underwater detection. On the basis of the conventional object detector, the spatial pyramid pooling module is designed, which reduces the number of computational parameters required for the extraction of features of the model while maintaining the same receptive field. This improves the inference efficiency of the network, and effectively alleviates the detection delay. The result shows that the improved model can identify underwater targets more accurately, and the detection speed of the model is also improved. The detection accuracy of the model achieved to 80.61% and the FPS achieved to 64.23.