Defect detection is a critical task in industrial manufacturing, playing a vital role in achieving automation, improving product quality, and ensuring operational safety. Traditional methods, however, face considerable limitations in terms of accuracy and efficiency. To address these challenges, we propose DCA-YOLO, a lightweight model for steel surface defect detection optimized based on task-specific knowledge. Specifically, our model incorporates a Dynamic Snake Convolution (DSConv) module to capture subtle linear features in challenging defect categories, a context-guided module to leverage contextual information for detecting clustered defects, and an Adaptive Spatial Feature Fusion (ASFF) mechanism to efficiently merge features across scales. The experimental results demonstrate that even with a nanoscale architecture (4.3 million parameters and 9.4 GFLOPs), the enhanced model exhibits marked improvements in detection accuracy and robustness, with mAP50 increasing by 4.6% and mAP50-95 by 7.7%. These findings not only offer a better solution for steel surface defect detection, but also provide new theoretical insights and practical experience for the advancement of industrial inspection technologies. In the future, DCA-YOLO is expected to be applied across a wider range of industrial detection scenarios, further driving progress in the field.