To address the challenges of low recognition accuracy and high computational complexity in underwater optical target recognition algorithms, a lightweight YOLOv8 underwater optical recognition algorithm based on automatic color equalization (ACE) image enhancement is proposed. Initially, we apply the ACE image enhancement algorithm to preprocess images. Subsequently, we improve the feature extraction capabilities by replacing the YOLOv8 backbone with an upgraded SENetV2 backbone network. To further decrease computational quantity, we introduce a lightweight cross-scale feature fusion module in place of the neck network. Then, we utilize DySample as a substitute for the traditional upsampler to improve image processing efficiency. We refine the DyHead detection head to better perceive targets. Finally, we enhance the accuracy of bounding box regression by replacing loss function of YOLOv8 with InnerMPDIoU based on the minimum point distance intersection ratio (MPDIoU). Experimental results show that the proposed SCDDI-YOLOv8 algorithm achieves a mean average precision of 77.3% and 71.5% on the URPC2020 and UWG datasets, respectively, while reducing parameters by ~20.7%, floating-point operations by 6×10⁸, and model size by 1.2 MB compared with the original YOLOv8n. Compared with other advanced algorithms, the proposed algorithm can meet the sensitive computational needs of edge devices.