To address the prevalent challenges of small target scales and occlusions encountered in port and ship operations scenarios, this work proposes the YOPR algorithm, which builds upon the YOLO framework. To tackle the aforementioned practical constraints, the following enhancements were introduced. Firstly, the feature extraction network was augmented with multiple ParNet Attention mechanisms. This improvement serves to optimize the deep convolutional layers’ capability to extract features from small-scale targets. Secondly, during the feature fusion process, the high-level convolutional outputs were replaced with those from shallower convolutions, enabling the retention of higher-resolution feature maps and thus more effective features for small targets. Evaluated on the self-constructed Person3100 dataset, the YOPR algorithm demonstrated satisfactory adaptability and robustness compared to other models, while meeting the real-time computational requirements of actual port monitoring systems.