An enhanced method for the detection of defective drainage pipes based on the YOLO v5l-Im algorithm was proposed. This method aims to address the low accuracy, high false detection, and leakage rate of the YOLO v5l (You Only Look Once version 5 large) algorithm for the detection of defective images of drainage pipes with small targets, few samples, and complex backgrounds. There are three enhancements. Initially, the Focal-EIoU (focal embedding intersection over union) loss function is suggested, which significantly enhances the detection model's performance. Secondly, the shallow feature map in the backbone network is fused into BiF-PN (bidirectional feature pyramid network) to increase the prediction layer for small targets, improving the detection effect of the detection model on small target defects and lowering the likelihood of defect misdetection and omission. Lastly, YOLO v5l introduces CA (coordinate attention) mechanism to lessen the interference of redundant background information and enhance the model's sensitivity to the image's region of interest. The value of mAP (mean average precision) showes improvements of 2.0, 2.9, and 5.9 percentage points, respectively, among the three improvements. The test findings indicate that the YOLO v5l-Im model suggested in this work obtains a mAP value of 92.1%, which is 6.5 percentage points higher than the 85.5% of the original model after fusing the three effective modifications. It is evident that the enhancements significantly increase the YOLO v5l's capacity to identify flaws in drainage pipes.