Abstract: Abstract: Underwater treasures, such as sea urchins, sea cucumbers, and scallops, have always been preferred in fish production, due mainly to the high value-added industry. However, two conventional approaches, including net fishing and manual catching, cannot meet the application requirements of rapid detection in the actual large-scale cultivation in modern agriculture, particularly on time-consuming, labor-intensive, and severe destruction of submarine environments in the early days. Alternatively, deep learning has widely been characterized by high resolution and fast speed in recent years. Therefore, it is a promising application potential to the target detection framework using the convolutional neural network in fishery production. It is also highly necessary to improve the detection performance in complex underwater environments. In this study, a YOLOv5 detection of underwater treasure was proposed using the attention mechanism, referred to as CG-YOLOv5, in order to provide a more accurate dataset for underwater robots. The main advantages were as follows: 1) DarkNet-53 was introduced the CBAM to deepen the network for the better performance of feature extraction, further to suppress the worthless features in the network. Specifically, the CBAM combined the channel and spatial attention to filter and weight the feature vectors. The channel attention focused mainly on what the detection target was, whereas, spatial attention was used to determine where the detection target was. As such, the prominent feature information was represented via two combined mechanisms, while weakening the general features. 2) The lightweight Ghost-Bottleneck module was introduced to replace the Bottleneck in YOLOv5. A simpler linear operation in Ghost-Bottleneck was utilized to maintain a higher accuracy with light weights. 3) New anchor points were obtained by clustering the labels of underwater datasets. A new detection scale was also added to the original three detections for higher detection accuracy. CG-YOLOv5 network mainly included CGDarknet-53 backbone network, Focus structure, Spatial Pyramid Pooling structure (SPP), and Path Aggregation Network (PANet). Focus served as a benchmark network with down sampling to change the input size of 640×640×3 to 320×320×32. Only one CSP structure was involved in the CG-YOLOv5 to integrate gradient changes completely into the feature map for feature fusion enhancement. The SPP structure was used to maximize the pooling of the feature layer. Four scales were utilized in the pooling layers with the pooling core sizes of 1×1, 5×5, 9×9, and 13×13, respectively. As such, the SPP effectively increased the perception field, while isolating significant contextual features. Furthermore, path aggregation networks were used to fuse different feature layers of an image. A specific dataset was also selected to verify the model using the actual underwater environment. There were 781 underwater images, 90% of which were employed as training datasets, and the rest were for testing. The experimental results demonstrated that the model fully met the requirement of detection and recognition for the treasures in complex underwater environments, compared with the current deep learning. The average accuracy was 95.67%. Compared with YOLOv5, the average precision of sea urchin, scallop and sea cucumber increased by 7.48, 6.90 and 2.09 percentage points, and mAP increased by 5.49 percentage points base point. Compared with other classical algorithms, the method has better accuracy and lower complexity. The finding can provide a more accurate and fast way to detect and capture aquatic products.