A drainage pipe defects detection approach that is based on the improved YOLO v5l algorithm is presented to solve the problems of low accuracy, high leakage and false detection rates of the YOLO v5l algorithm for drainage pipe defect image detection with small targets, few samples and complex backgrounds. Three improvements are made: firstly, a loss function is suggested based on Focal EIoU, which can effectively raise the capability of detection model; secondly, in order to enhance the detection effectiveness of the model for small objectives and lower the rate of false detection and leakage, the shallow feature map in the backbone network is fused into the BiFPN feature fusion network to add a prediction layer for small objectives; finally, in order to improve the sensitivity of the model to the sensitivity of the model to the interested region in the image and decrease the pleonatic intervention of background information, the CA attention module is introduced in YOLO v5l. The three improvements improve the average accuracy mAP values by 2.0, 2.9, and 5.9 percentage points, respectively. Combining the three effective improvements together, the test results show that the improved YOLO v5l model proposed in this paper achieves an mAP value of 92.1%, which is 6.5 percentage points higher than the 85.5% of the original model. This shows that the improvements made effectively enhance the detection capability of YOLO v5l for drainage pipe defects.