Light weight detection of mango surface defects based on machine vision

Abstract

Objective: Reduce the manufacturing cost of automated mango grading equipment. Methods: The effects of three commonly detection algorithms for mango defect detection were compared, and a defect detection algorithm based on YOLOv5 for mango surface was proposed for the light weight design to work on mobile devices. Results: Compared with the original algorithm, the experimental algorithm can reduce the number of parameters by 45.9%, the number of floating point operations by 46.7%, and the weight file size by 45.2% under the premise of meeting the requirements for mango surface defect detection. Conclusion: the experimental algorithm effectively reduces the performance requirements for deployment equipment, and has potential value in reducing the manufacturing cost of mango grading detection equipment.

Keywords

• mango
• fruit surface detection
• neural network
• light weight
• machine vision
• image identification