Aiming at the problems of missing detection, false detection, and low detection accuracy of the Faster R-CNN target detection network, a soft non-maximum suppression (Soft-NMS) fusion attention mechanism and the Faster R-CNN (Faster Region-Convolutional Neural Network) target detection algorithm is proposed. In order to enhance the global important feature extraction and weaken the irrelevant feature in the feature map by the Faster R-CNN target detection algorithm, an attention mechanism is firstly introduced into the network. Second, aiming at the problem of local information loss caused by the bottleneck structure formed by two fully connected layers in the attention mechanism, a non-dimensional-reduction channel attention and spatial attention series module that can be trained end-to-end with the convolutional neural network is constructed. Then, a Soft-NMS is introduced to replace the traditional non-maximal suppression (NMS) algorithm after the regional suggestion network, which can reduce the target missing detection and improve the location accuracy. Finally, the error detection rate is introduced into the evaluation criteria to further verify the performance of the model. Experimental results show that the Faster R-CNN algorithm based on ResNet-50 can effectively reduce the missed detection and false detection and improve the location accuracy, and the average detection accuracy is significantly improved.