Receptive field enhancement and attention feature fusion network for underwater object detection

Underwater environments have characteristics such as unclear imaging and complex backgrounds that lead to poor performance when applying mainstream object detection models directly. To improve the accuracy of underwater object detection, we propose an object detection model, RF-YOLO, which uses a receptive field enhancement (RFE) module in the backbone network to finish RFE and extract more effective features. We design the free-channel iterative attention feature fusion module to reconstruct the neck network and fuse different scales of feature layers to achieve cross-channel attention feature fusion. We use Scylla-intersection over union (SIoU) as the loss function of the model, which makes the model converge to the optimal direction of training through the angle cost, distance cost, shape cost, and IoU cost. The network parameters increase after adding modules, and the model is not easy to converge to the optimal state, so we propose a training method that effectively mines the performance of the detection network. Experiments show that the proposed RF-YOLO achieves a mean average precision of 87.56% and 86.39% on the URPC2019 and URPC2020 datasets, respectively. Through comparative experiments and ablation experiments, it was verified that the proposed network model has a higher detection accuracy in complex underwater environments.