With the continuous development and wide applications of deep learning, target detection algorithms based on deep learning have become a new mainstream. To further improve the detection accuracy of the convolutional neural network YOLO v3 (You only look once v3), a convolution layer module was added to the network structure of the original algorithm to classify the target background of the sample and the anchor frame size of the feature map was roughly adjusted. To resolve the challenge of unbalanced proportion of positive and negative samples in the original algorithm, samples with background probability value less than the set threshold value were filtered by the module after outputting the target background probability. The adjusted anchor box was used to replace the anchor box of fixed sizes directly generated by clustering in the original algorithm. This process provides a better initial value for bounding box prediction. Experimental results on VOC dataset indicate that the improved YOLO v3 shows higher detection accuracy than the original algorithm.