Multi-target detection model construction method based on convolutional neural network

The invention discloses a multi-target detection model construction method based on a convolutional neural network to solve the technical problem that the existing detection model cannot distinguish multiple targets and is difficult to identify small targets. The method comprises the following steps: 1: constructing a Caffe deep learning framework, wherein the configuration of the detection modelis completed by using a Faster R-CNN algorithm, and a ZF network is introduced for feature extraction; 2, designing an ADPN network to accurately generate a multi-target area in real time; 3, designing and optimizing the loss function of the ADPN; 4, training the ADPN; 5, designing a DALN sub-network for detecting multi-target categories and locations; 6, designing and optimizing the loss functionof the DALN; 7, training the DALN; and 8, jointly training the ADPN and the DALN to obtain a detection model. The method can identify the targets of multiple categories, improve the recognition ability for the small targets,