Concerning the problem that the surface defects of the coin are small， variable in shape， easily confused with the background and difficult to be detected， an improved algorithm of coin surface defect detection named DCA-YOLO （Deformable Convolution and Adaptive space feature fusion-YOLO） was proposed. First of all， due to the different shapes of defects， three network structures with deformable convolution modules added at different positions in the backbone network were designed， and the ability to extract defects was improved through convolution learning offset and adjusting parameters. Then， the adaptive spatial feature fusion network was used to learn the weight parameters to better adapt to targets with different scales by adjusting the contribution of each pixel in the feature maps of different scales. Finally， the anchor ratio was adjusted， the category weights were dynamically adjusted， the comparison network performance was optimized， thus， a model network to add deformable convolution before upsampling for multi-scale fusion of the output features of the backbone network was proposed. Experimental results show that on the coin defect dataset， the detection mAP （mean Average Precision） of DCA-YOLO algorithm reaches 92.8%， which is close to that of Faster-RCNN （Faster Region-based Convolutional Neural Network）； compared with YOLOv3， the proposed algorithm has the detection speed basically the same with 3.3 percentage points improvement on detection mAP， and 3.2 percentage points increase on F1-score.