Aiming at the problems of complex multi-target image detection scenes and redundant target position data with different length, width and height, the neural network algorithm can effectively improve the accuracy and stability of parallel detection of different types of targets. A multi-target detection method based on the improved YOLOv5 network is proposed. First, according to the spatial scale of different objects, the feature fusion method of the model is improved, and a multi-scale feature detection layer is added to reduce the error of multi-target detection. At the same time, Adaptive Feature Adjustment module is added to reduce the false detection rate and missed detection rate of the network; then K-means++ algorithm is used to estimate the candidate frame to obtain better frame parameters; finally, Efficient IOU Loss is used in the loss function for optimization. Experiments show that the mean average precision of the improved method reaches 76.48%, which is 3.2% higher than the classic YOLOv5 network, and the average detection accuracy of small-sized objects increases by 6.3%. The improved method network continues the lightweight and high-efficiency of the YOLOv5 network, obtains better detection accuracy for multi-scale target detection and can achieve more accurate real-time multi-target detection.