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Research on Multi-Scenario Fruit Detection and Classification Based on Improved YOLOv5

Year: 2025

Type: article

Abstract: This study addresses the challenge of efficient fruit detection and accurate classification in complex environments. Based on the YOLOv5 framework, a training set was constructed by combining publicly available and self-collected datasets, focusing on three types of fruits: bananas, apples, and oranges. The study incorporated data augmentation, transfer learning, the CBAM (Convolutional Block Attention Module) attention mechanism, and an improved Non-Maximum Suppression (NMS) algorithm. Experimental results demonstrated an average precision of 96.5% and an inference speed of 39.26 frames per second under complex scenarios. Coupled with a user-friendly interface design, this system provides an effective real-time detection and statistical solution for agriculture, retail, and food processing industries.

Source: Applied and Computational Engineering

DOI: 10.54254/2755-2721/2025.23113

Author: Xinyang Li

Institution: Beijing University of Civil Engineering and Architecture

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: hybrid

OpenAlex ID: W4410474841

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