# Localizing Small Apples in Complex Apple Orchard Environments

arXiv: https://arxiv.org/abs/2202.11372 · HTML: https://mlsanity.com/paper/2202.11372/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Learning to Segment Object Candidates](https://mlsanity.com/paper/1506.06204/) (2015)
- [Learning to Refine Object Segments](https://mlsanity.com/paper/1603.08695/) (2016)
- [MinneApple: A Benchmark Dataset for Apple Detection and Segmentation](https://mlsanity.com/paper/1909.06441/) (2019)
- [What makes for effective detection proposals?](https://mlsanity.com/paper/1502.05082/) (2015)
- [Deep Fruit Detection in Orchards](https://mlsanity.com/paper/1610.03677/) (2016)
- [A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards](https://mlsanity.com/paper/1810.09499/) (2018)
- [FastMask: Segment Multi-scale Object Candidates in One Shot](https://mlsanity.com/paper/1612.08843/) (2016)
- [AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects](https://mlsanity.com/paper/1811.08728/) (2018)
- [Superpixel-based Refinement for Object Proposal Generation](https://mlsanity.com/paper/2101.04574/) (2021)
- [DeepFH Segmentations for Superpixel-based Object Proposal Refinement](https://mlsanity.com/paper/2108.03503/) (2021)

## Built on by (most cited first, 5 of 5)

- [S3AD: Semi-supervised Small Apple Detection in Orchard Environments](https://mlsanity.com/paper/2311.05029/) (2023)
- [Small, but important: Traffic light proposals for detecting small traffic lights and beyond](https://mlsanity.com/paper/2307.15191/) (2023)
- [Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask](https://mlsanity.com/paper/2203.11358/) (2022)
- [Phenology-based learning framework for yield estimation and harvest forecasting of raspberry fruits](https://mlsanity.com/paper/2411.00967/) (2024)
- [AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards](https://mlsanity.com/paper/2505.14029/) (2025)

## Abstract (arXiv)

The localization of fruits is an essential first step in automated agricultural pipelines for yield estimation or fruit picking. One example of this is the localization of apples in images of entire apple trees. Since the apples are very small objects in such scenarios, we tackle this problem by adapting the object proposal generation system AttentionMask that focuses on small objects. We adapt AttentionMask by either adding a new module for very small apples or integrating it into a tiling framework. Both approaches clearly outperform standard object proposal generation systems on the MinneApple dataset covering complex apple orchard environments. Our evaluation further analyses the improvement w.r.t. the apple sizes and shows the different characteristics of our two approaches.