Efficient Apple Maturity and Damage Assessment: A Lightweight Detection Model with GAN and Attention Mechanism

TL;DR

This paper introduces a lightweight CNN model enhanced with GAN-generated data and attention mechanisms for accurate, real-time apple ripeness and damage detection, outperforming existing models.

Contribution

The study presents a novel lightweight detection framework combining optimized CNN, GAN data augmentation, and attention mechanisms for improved apple maturity and damage assessment.

Findings

Achieved over 95% precision and recall in ripeness detection.

Improved damage detection accuracy with 94.5% mAP.

Enhanced real-time performance with high FPS.

Abstract

This study proposes a method based on lightweight convolutional neural networks (CNN) and generative adversarial networks (GAN) for apple ripeness and damage level detection tasks. Initially, a lightweight CNN model is designed by optimizing the model's depth and width, as well as employing advanced model compression techniques, successfully reducing the model's parameter and computational requirements, thus enhancing real-time performance in practical applications. Simultaneously, attention mechanisms are introduced, dynamically adjusting the importance of different feature layers to improve the performance in object detection tasks. To address the issues of sample imbalance and insufficient sample size, GANs are used to generate realistic apple images, expanding the training dataset and enhancing the model's recognition capability when faced with apples of varying ripeness and damage levels. Furthermore, by applying the object detection network for damage location annotation on damaged apples, the accuracy of damage level detection is improved, providing a more precise basis for decision-making. Experimental results show that in apple ripeness grading detection, the proposed model achieves 95.6\%, 93.8\%, 95.0\%, and 56.5 in precision, recall, accuracy, and FPS, respectively. In apple damage level detection, the proposed model reaches 95.3\%, 93.7\%, and 94.5\% in precision, recall, and mAP, respectively. In both tasks, the proposed method outperforms other mainstream models, demonstrating the excellent performance and high practical value of the proposed method in apple ripeness and damage level detection tasks.