In order to solve the problem that YOLOv5 algorithm is not accurate in locating the target object and local features,a YOLOv5-ABN target detection network based on YOLOv5 is proposed.Firstly,in order to get accurate positioning information,this paper combines spatial attention mechanism and coordinate attention module in convolution attention mechanism.A spatial and coordinated attention module is proposed,which integrates spatial feature information and coordinate information.The spatial and coordinated attention module is added to the Backbone network of YOLOv5.The weighted bidirectional feature pyramid network is introduced into the Neck detection layer,and the staggered box parameters are optimized,which improves the detection accuracy and positioning ability of the network for sample models and local features.The customized data set and Cornell data set are trained with the improved algorithms respectively.The experiment results show that the improved algorithm is better than YOLOv5 algorithm,and the target object is more accurate and more robust without reducing the recognition accuracy.