Target detection algorithm based on wheat image

Abstract

The invention relates to a target detection algorithm based on a wheat image, and the detection algorithm comprises the following steps: 1, a first stage is a training stage of the algorithm, and 2, asecond stage is a real-time reasoning stage of the algorithm. The YOLOv5 algorithm after wheat data set optimization has high precision and high reasoning speed, real-time reasoning can be achieved,offline training of the YOLOv5 algorithm can also be completed on a single GPU (such as GTX1080ti or RTX2080Ti), and the requirement for hardware is greatly reduced.

Description

technical field [0001] The invention relates to a detection algorithm, in particular to a target detection algorithm based on wheat images, and belongs to the technical field of artificial intelligence computer vision. Background technique [0002] Target detection is a basic direction in the field of artificial intelligence computer vision, and the usual target detection belongs to supervised learning. Given a data set and labels, the category and location of the target contained in the given data set are trained through preprocessing, feature extraction, feature fusion, and detection, respectively, so as to predict the target contained in similar pictures outside the training set Purpose of Category and Location. [0003] The target detection algorithm mainly has three components: backbone, neck and head. Among them, backbone is the part of feature extraction. Common backbones include VGG, resnet, darknet, etc.; neck is the feature fusion part. Since the advent of featur...

AI Technical Summary

Problems solved by technology

However, target detection algorithms still face different problems in specific applications, such as: few data set samples, occlusion problems, small targets, aggregation, category imbalance, etc.

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