Underwater Object Detection Algorithm Based on Improved CenterNet

Aiming at the problems of conventional detectors in detecting underwater objects, such as difficulty in feature extraction and missing detection of objects, an improved CenterNet underwater object detection method is proposed. First, a high resolution human posture estimation network HRNet is used to replace the Hourglass-104 backbone network in CenterNet model to reduce the amount of parameters and improve the speed of network reasoning; then, the bottleneck attention module is introduced to enhance the features in the spatial and channel dimensions, and improve the detection accuracy; finally, a feature fusion module is constructed to integrate the rich semantic information and spatial location information in the network, the fused features are processed by receptive field block to further improve the multi-scale object detection ability of the network. A comparison experiment is carried out on the URPU underwater object detection dataset. Compared with CenterNet network, the detection accuracy of the proposed algorithm can reach 77. 4%, increased by 1. 5 percentage points, the detection speed is 7 frame/s, increased by 35. 6%, the amount of parameters is 30. 4 MB, compressed by 84. 1%. Compared with the mainstream object detection algorithm, this algorithm also has higher detection accuracy, which has higher advantages in underwater object detection.