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

arXiv: https://arxiv.org/abs/2409.13330 · HTML: https://mlsanity.com/paper/2409.13330/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://mlsanity.com/paper/1803.03243/) (2018)
- [Bounding Box Regression with Uncertainty for Accurate Object Detection](https://mlsanity.com/paper/1809.08545/) (2018)
- [Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild](https://mlsanity.com/paper/2007.12107/) (2020)
- [Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions](https://mlsanity.com/paper/2112.08088/) (2021)
- [Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images](https://mlsanity.com/paper/2205.13764/) (2022)
- [Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels](https://mlsanity.com/paper/2005.08455/) (2020)
- [LocNet: Improving Localization Accuracy for Object Detection](https://mlsanity.com/paper/1511.07763/) (2015)
- [Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts](https://mlsanity.com/paper/2009.02967/) (2020)

## Abstract (arXiv)

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.