Abstract: Intelligent detection of marine organisms is a significant step of marine ranching strategy. An underwater robot is highly demanding to rapidly and accurately monitor marine organisms in the complex ocean environment. However, there is a relatively low distinction between marine organisms and their living environment, some of which are covered or semi-hidden, due mainly to the low contrast of seabed environment, and uneven distribution of brightness. Therefore, it is a big challenge to accurately identify the specific marine life in the undersea environment. Many target (object) detections using deep learning have emerged, such as EfficientDet, RetinaNet, and YOLO-V4, with the development of convolutional neural networks (CNN) in recent years. Nevertheless, the current network cannot fully meet the specific requirements of marine biological recognition. It is also necessary to improve the detection accuracy, operation speed, and detection efficiency of dense targets. In this study, an improved target (object) detection network using YOLO-V4 was designed to realize the rapid and accurate identification of marine organisms in an aquaculture environment of a shallow sea. A marine biological dataset was firstly established with 7 240 images, which were generated from 1 810 original images after data enhancement. Training (80%) and test datasets (20%) were divided. Data enhancement (suitable for the small data sample learning) effectively enriched the background and elements of the original images, thereby producing much more learning samples than before. As such, an effective expansion of the sample was achieved in the same learning effect as the large sample. Secondly, the Cross-Stage Partial network (CSP) was successfully introduced, while the Embedded Connection (EC) component was designed to detect marine organisms. An improved YOLO-V4 network model was constructed, when the EC was embedded into the end of the YOLO-V4 network. The improved YOLO-V4 network with an EC can be expected to make the gradient flow propagate on different learning paths, while effectively delay the occurrence of gradient disappearance, aiming to improve the detection accuracy and cost-saving calculation. Finally, Marine Organism Detection (MOD) was presented using the improved YOLO-V4 network to achieve a better performance in the complex seabed environments. The experimental results showed that the mAP50 and mAP75 of the MOD model were 0.969 and 0.734, respectively, while the computational complexity was 35.328 billion floating-point operations (BFLOPs), and the detection frame rate was 139 ms on the computer system with a graphics accelerator GeForce GTX 1650. The mAP50 and mAP75 from the MOD increased by 0.9 percent points and 4.8 percent points, respectively, while the amount of computation only increased by 0.2%, compared with the original YOLO-V4 model. Especially, the evaluating indicators in the MOD model improved in all studied categories, where mAP75 presented the most obvious. In addition, the precision and recall values of balance points in the MOD model were closer to (1, 1) in most cases. It can also be reasonable that the learning performance was better in the MOD than the original YOLO-V4 model, compare with the PR curves. Consequently, the finding can provide promising insightful ideas and useful references for the rapid and accurate detection of the marine organisms in an underwater robot of intelligent fishing.