# Using depth information and colour space variations for improving outdoor robustness for instance segmentation of cabbage

arXiv: https://arxiv.org/abs/2103.16923 · HTML: https://mlsanity.com/paper/2103.16923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [Agricultural Robotics: The Future of Robotic Agriculture](https://mlsanity.com/paper/1806.06762/) (2018)
- [MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification](https://mlsanity.com/paper/1907.11111/) (2019)

## Built on by (most cited first, 1 of 1)

- [Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search](https://mlsanity.com/paper/2210.01384/) (2022)

## Abstract (arXiv)

Image-based yield detection in agriculture could raiseharvest efficiency and cultivation performance of farms. Following this goal, this research focuses on improving instance segmentation of field crops under varying environmental conditions. Five data sets of cabbage plants were recorded under varying lighting outdoor conditions. The images were acquired using a commercial mono camera. Additionally, depth information was generated out of the image stream with Structure-from-Motion (SfM). A Mask R-CNN was used to detect and segment the cabbage heads. The influence of depth information and different colour space representations were analysed. The results showed that depth combined with colour information leads to a segmentation accuracy increase of 7.1%. By describing colour information by colour spaces using light and saturation information combined with depth information, additional segmentation improvements of 16.5% could be reached. The CIELAB colour space combined with a depth information layer showed the best results achieving a mean average precision of 75.