Crane defect detection traditionally relies on manual inspection and image processing techniques, which struggle with complex defect types and subtle features. Although deep learning has improved detection performance, challenges with insufficient detection capability persist. We propose a defect recognition and localization method for crane components using the YOLOv5s-CUA fusion enhancement algorithm. A dataset specifically for floating crane defects is first established. The YOLOv5s model is then enhanced by replacing nearest-neighbor interpolation up-sampling with the content-aware reassembly of features operator to improve feature map completeness, integrating the convolutional block attention module with the C3 module to boost important feature channel extraction, and incorporating the adaptive structure feature fusion module to enhance adaptive fusion capabilities. Experimental results demonstrate that the improved YOLOv5s model increases detection accuracy by 2% and recall rate by 3.7% compared with the original YOLOv5s model, significantly reducing leakage and misdetection. This advancement enables rapid identification of defects in floating crane components and provides robust technical support for practical applications.