The problems of large number of targets， small scale and high-overlapping lead to low accuracy and difficulty in target detection. In order to improve the precision of target detection and avoid missed detection and false detection as much as possible， an improved YOLOv3 target detection algorithm based on boundary limit point features was proposed. Firstly， a boundary enhancement operator Border was introduced to adaptively extract boundary features from the limit points of the boundary to enhance the features of the existing points and improve the accuracy of target positioning. Then， the precision of target detection was further improved by increasing the target detection scale， refining the feature map， and enhancing the fusion of the feature image deep and shallow semantic information. Finally， based on the target instance characteristics in target detection and the improved network model， the Complete Intersection over Union （CIoU） function was introduced to improve the original YOLOv3 loss function， thereby improving the convergence speed and recall of the detection box. Experimental results show that compared with the original YOLOv3 target detection algorithm， the improved YOLOv3 target detection algorithm has the Average Precision increased by 3.9 percentage points ， and has the detection speed similar to the original algorithm， verifying that it can effectively improve the target detection ability of models.