In the task of small target defect detection on steel cables, there are common problems such as low detection accuracy, high missed detection rate and frequent false detection, which are particularly obvious in the detection scenario with more small sizes. The main reasons for such problems include: insufficient feature extraction capability of traditional detection algorithms, lack of effective multi-scale information fusion mechanism, and insensitivity of existing loss functions to small targets. To address the above problems, a steel cable defect detection method based on improved RT-DETR is proposed. The BasicStar feature extraction module was designed in the backbone network to improve the semantic representation ability of the model in high-dimensional space; at the same time, a new multi-scale feature fusion strategy small object pyramid network(SOPN) is designed to strengthen the attention and expression ability of small targets; in terms of loss function, a focal enhancement Focaler-SIoU loss function is proposed to improve the positioning accuracy of small targets and the stability of training convergence. Experimental results on the steel cable defect dataset show that the improved model improves the average detection accuracy mAP50 by 2.1% compared with the original RT-DETR. The comprehensive performance is better than the existing mainstream target detection algorithms, which verifies the effectiveness and practicality of the proposed method for small target defect detection tasks in industrial scenarios.