Enhancing Fruit and Vegetable Detection in Unconstrained Environment with a Novel Dataset

• URL: http://arxiv.org/abs/2409.13330v1
• Date: Fri, 20 Sep 2024 08:46:03 GMT
• Title: Enhancing Fruit and Vegetable Detection in Unconstrained Environment with a Novel Dataset
• Authors: Sandeep Khanna, Chiranjoy Chattopadhyay, Suman Kundu,
• Abstract summary: This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. We have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations.
• Score: 4.498047714838568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results.

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