Lychee Surface Defect Detection Based on Deep Convolutional Neural Networks with GAN-Based Data Augmentation

• Published in: Agronomy (Basel) Vol. 11; no. 8; p. 1500
• Main Authors: Wang, Chenglong, Xiao, Zhifeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2021
• Subjects: agronomy; Artificial neural networks; Automation; Background noise; Categories; cost effectiveness; Data augmentation; data collection; Datasets; deep convolutional neural network; Deep learning; Defects; Design; Faster RCNN; Foliage; Fruits; generative adversarial network; Generative adversarial networks; leaves; litchis; Low temperature; lychee; Neural networks; Occlusion; Rot; SSD; surface defect detection; Surface defects; Surface properties; surface quality
• DOI: 10.3390/agronomy11081500
• ISSN: 2073-4395

The performance of fruit surface defect detection is easily affected by factors such as noisy background and foliage occlusion. In this study, we choose lychee as a fruit type to investigate its surface quality. Lychees are hard to preserve and have to be stored at low temperatures to keep fresh. Additionally, the surface of lychees is subject to scratches and cracks during harvesting/processing. To explore the feasibility of the automation of defective surface detection for lychees, we build a dataset with 3743 samples divided into three categories, namely, mature, defects, and rot. The original dataset suffers an imbalanced distribution issue. To address it, we adopt a transformer-based generative adversarial network (GAN) as a means of data augmentation that can effectively enhance the original training set with more and diverse samples to rebalance the three categories. In addition, we investigate three deep convolutional neural network (DCNN) models, including SSD-MobileNet V2, Faster RCNN-ResNet50, and Faster RCNN-Inception-ResNet V2, trained under different settings for an extensive comparison study. The results show that all three models demonstrate consistent performance gains in mean average precision (mAP), with the application of GAN-based augmentation. The rebalanced dataset also reduces the inter-category discrepancy, allowing a DCNN model to be trained equally across categories. In addition, the qualitative results show that models trained under the augmented setting can better identify the critical regions and the object boundary, leading to gains in mAP. Lastly, we conclude that the most cost-effective model, SSD-MobileNet V2, presents a comparable mAP (91.81%) and a superior inference speed (102 FPS), suitable for real-time detection in industrial-level applications.