A CNN-Based Image Detection System for Full-Surface Defects in Brown Rice
TL;DR
This study demonstrates the feasibility of CNN-based brown rice defect detection, with future work directed toward lightweight deployment and multimodal fusion for production-line application.
Abstract
In order to enable accurate and efficient rice quality evaluation through full-surface defect detection of brown rice, a detection system based on convolutional neural network (CNN) was developed. A dataset of images of five categories—unhulled, normal, broken, cracked, and insect-bitten brown rice—was collected. Three CNN models, YOLOv5s, YOLOv7, and Faster R-CNN, were evaluated and compared with traditional algorithms including support vector machine (SVM) and back propagation (BP) neural networks. Experimental results showed that CNN-based methods in the present database significantly outperformed traditional approaches, with the YOLOv5s model achieving the best comprehensive performance: 95.80% detection accuracy, 10.90 ms inference time per image, and 92 frames/s processing speed. An improved Rice-YOLOv5s algorithm was further proposed and validated through batch detection experiments, achieving an average recognition accuracy of 96.44% and a processing time of 9.2 ms per image, which is equivalent to approximately 108.7 FPS. This study demonstrates the feasibility of CNN-based brown rice defect detection, with future work directed toward lightweight deployment and multimodal fusion for production-line application.