Target detection method based on YOLO-Terse network and storage medium

Abstract

The invention discloses a target detection method based on a YOLO-Terse network. The method comprises the steps of obtaining a to-be-detected image containing a to-be-detected target; inputting the to-be-detected image into a pre-trained YOLO-Terse network, and determining the category to which the to-be-detected target belongs and the position of the to-be-detected target in the to-be-detected image according to the features of the to-be-detected image, wherein the YOLO-Terse network is formed by adopting hierarchical and channel-level pruning on the basis of the YOLOv3 network and guiding the network to recover by combining knowledge distillation. According to the invention, layer pruning, sparse training, channel pruning and knowledge distillation processing are carried out on the YOLOv3, optimized processing parameters are selected, the simplified YOLO-Terse network is obtained, the size of the network is greatly reduced, most redundant calculation is eliminated, the target detection speed based on the network is greatly increased, and the detection precision can be maintained.

Description

technical field [0001] The invention belongs to the technical field of target detection methods, and in particular relates to a target detection method and a storage medium based on a YOLO-Terse network. Background technique [0002] Object detection can accurately classify and locate objects in images or videos, and plays a vital role in surveillance, unmanned driving, mechanical automation and other fields. [0003] In today's more mainstream target detection framework, the YOLOv3 network performs well in terms of the balance between detection speed and accuracy. People continue to use the YOLOv3 network to achieve target detection functions in various fields. However, the original YOLOv3 network does not perform well in various data sets, especially when there are relatively few types of detection targets, such as detecting pedestrians and vehicles on campus, using the existing YOLOv3 network will be redundant. In addition, it increases unnecessary calculations and reduc...

AI Technical Summary

Problems solved by technology

However, the original YOLOv3 network does not perform well in various data sets, especially when there are relatively few types of detection targets, such as detecting pedestrians and vehicles on campus, using the existing YOLOv3 network will be redundant. In addition, it increases unnecessary calculations and reduces the speed of target detection; especially when the YOLOv3 network is deployed on edge devices, it is particularly important to speed up by simplifying the YOLOv3 network model

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