Detection and Counting of Small Target Apples under Complicated Environments by Using Improved YOLOv7-tiny

Author:

Ma Li1,Zhao Liya1,Wang Zixuan1,Zhang Jian23ORCID,Chen Guifen4

Affiliation:

1. College of Information Technology, Jilin Agricultural University, Changchun 130118, China

2. Faculty of Agronomy, Jilin Agricultural University, Changchun 130118, China

3. Department of Biology, University of Columbia Okanagan, Kelowna, BC V1V 1V7, Canada

4. Institute of Technology, Changchun Humanities and Sciences College, Changchun 130118, China

Abstract

Weather disturbances, difficult backgrounds, the shading of fruit and foliage, and other elements can significantly affect automated yield estimation and picking in small target apple orchards in natural settings. This study uses the MinneApple public dataset, which is processed to construct a dataset of 829 images with complex weather, including 232 images of fog scenarios and 236 images of rain scenarios, and proposes a lightweight detection algorithm based on the upgraded YOLOv7-tiny. In this study, a backbone network was constructed by adding skip connections to shallow features, using P2BiFPN for multi-scale feature fusion and feature reuse at the neck, and incorporating a lightweight ULSAM attention mechanism to reduce the loss of small target features, focusing on the correct target and discard redundant features, thereby improving detection accuracy. The experimental results demonstrate that the model has an mAP of 80.4% and a loss rate of 0.0316. The mAP is 5.5% higher than the original model, and the model size is reduced by 15.81%, reducing the requirement for equipment; In terms of counts, the MAE and RMSE are 2.737 and 4.220, respectively, which are 5.69% and 8.97% lower than the original model. Because of its improved performance and stronger robustness, this experimental model offers fresh perspectives on hardware deployment and orchard yield estimation.

Funder

National Natural Science Foundation of China–Joint Fund

Strategic Research and Consulting Project of Chinese Academy of Engineering

Jilin Provincial Department of Education Project

Jilin province science and technology development plan project

Jilin University Student Innovation Training Program

Publisher

MDPI AG

Subject

Agronomy and Crop Science

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