MIP-YOLO11: An Underwater Object Detection Model Based on Improved YOLO11

Due to challenges such as inadequate lighting, water scattering, high density of small objects, and complex object morphology in underwater environments, traditional YOLO11 models face difficulties including interference from complex backgrounds, weak perception of small objects, and insufficient feature extraction when applied underwater. This paper proposes an improved MIP-YOLO11 model for underwater object detection based on the YOLO11 framework. First, a MCEA module is designed in the backbone network to replace the basic CBS convolution module. Through a lightweight multi-branch convolutional structure, the perception ability for small objects, object edges, contours, and morphological features in underwater scenes are enhanced without significantly increasing computational overhead. Second, an IMCA module based on the coordinate attention mechanism is introduced at the end of the backbone network to replace the C2PSA module, reducing the number of model parameters while maintaining detection accuracy. Finally, the Bottleneck module in C3k2 is improved by incorporating a PConv and a dual residual connection mechanism, thereby expanding the receptive field and enhancing the efficiency of complex feature extraction. Experimental results demonstrate that MIP-YOLO11 significantly outperforms the traditional YOLO11 in underwater environments. P and R are improved by 2.5% and 4.1%, respectively. Moreover, the mAP0.5 and mAP0.5:0.95 metrics are increased by 4.2% and 7.5%, respectively. The improved model achieves a good balance between high accuracy and light weight, and can provide a more reliable underwater object detection scheme for AUV underwater detection and other application scenarios.