Real-time detection method of seafood for intelligent construction of marine ranch

Object detection for seafood is a key to intelligence marine ranching, but its real-time and accuracy need to be improved. In this paper we present an YOLOv3-based real-time object detection method for seafood. Firstly, for better real-time performance, we replace the 3×3 standard convolution in YOLOv3 with the depthwise separable convolution technology. This technology resolves standard convolution into depthwise convolution and pointwise convolution. It greatly reduces the amount of parameters and calculations of the model. Thus, we obtain a lightweight network model, named Depthwise Separable Convolution-YOLO (DSC-YOLO); Then, in the data preprocessing stage, we introduce UGAN (Underwater Generative Adversarial Network) based image enhancement methods to relief the color distortion of underwater seafood images. This method uses an unsupervised method to automatically learn the distribution of training data and generate new data. The network consists of generator network and discriminators network. Its main idea is confronting and gaming between networks, on the basis of original data information to generate a new data information. It realizes the mapping process from underwater blurred image to underwater clear image. To be specific, we use CycleGAN to convert images in air to underwater-like images. Thus, lots of image pairs can be obtained and used to train UGAN model. Once UGAN finish training, learned generator of UGAN can transform real underwater images to clear images. Finally, we introduce Mosaic-based data augment methods to enrich image diversity. To be specific, four images are randomly selected from underwater dataset images, and operated with traditional data augmentation (rotation, translation, scaling, roll). Then, the four images are randomly cropped and spliced, obtaining a Mosaic image. The new image has the same dimension as the original images. Combining the three strategies, we obtain a real-time object detection method for seafood. In order to verify the effectiveness of our proposed method, we conduct several experiments. In these experiments, DSC-YOLO, UGAN underwater image enhancement and Mosaic data enhancement were combined to obtain four models: DSC-YOLO, DSC-YOLO+UGAN, DSC-YOLO+Mosaic, DSC-YOLO+UGAN+Mosaic. The four models compare with the YOLOv3 model over the same dataset. The results of experiments show that compared with YOLOv3 model, the model size of the proposed method reduces by 70%, the inference time reduces by 16 percentage point, the recall improves by 2.7 percentage point, the mean Average Precision 0.5 (mAP0.5) promotes by 2.4 percentage point, and the F1-score increases by 0.4 percentage point. The results indicate that the proposed method has the potential to meet real-time requirements and deploy on mobile devices.