With an aim to address the issues of low precision and susceptibility to background interference in steel surface defect detection, a YOLOv10n target detection algorithm based on fusion and shared parameters is proposed. Firstly, the backbone network incorporates the enhanced FasterNet lightweight network and the channel-first convolutional attention mechanism to enhance the capacity of the backbone network in representing multidimensional information. Secondly, the PCONV-C2F module is designed based on partial convolution (PConv) to tackle the problem of the disparity in the sensitivity field of the C2f module. Thirdly, wavelet pooling is utilized to address the problem of aliasing and background interference resulting from the up and down sampling mechanism in the original algorithm. Finally, a lightweight detection head is put forward to reduce the computational complexity of the model and enhance the accuracy of bounding box prediction by integrating shared parameters with dynamic distribution techniques. The mean average precision (mAP) mAP@0.5 of the improved algorithm on the NEU-DET dataset attains 86.3%, which is 8.1% higher than that of the original algorithm, and the precision reaches 86.8%, which is 18.7% higher than that of the original algorithm. The ablation and comparison experiments demonstrate that the improved algorithm exhibits excellent performance in the surface defect detection of steel and metal materials, which not only meets the requirement for efficient and accurate detection of steel surface defects in practical applications, but also significantly enhances the reliability and practicability of the detection.