# Deep Learning Based Robot for Automatically Picking up Garbage on the Grass

arXiv: https://arxiv.org/abs/1904.13034 · HTML: https://mlsanity.com/paper/1904.13034/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning](https://mlsanity.com/paper/2302.02976/) (2023)
- [Robotic Waste Sorter with Agile Manipulation and Quickly Trainable Detector](https://mlsanity.com/paper/2104.01260/) (2021)
- [Collaborative Perception in Multi-Robot Systems: Case Studies in Household Cleaning and Warehouse Operations](https://mlsanity.com/paper/2408.14039/) (2024)
- [Real-Time Trash Detection for Modern Societies using CCTV to Identifying Trash by utilizing Deep Convolutional Neural Network](https://mlsanity.com/paper/2109.09611/) (2021)
- [New Benchmark for Household Garbage Image Recognition](https://mlsanity.com/paper/2202.11878/) (2022)
- [A Waste Copper Granules Rating System Based on Machine Vision](https://mlsanity.com/paper/2207.04575/) (2022)
- [Searching for Uncollected Litter with Computer Vision](https://mlsanity.com/paper/2211.14743/) (2022)
- [Visual Material Characteristics Learning for Circular Healthcare](https://mlsanity.com/paper/2309.04763/) (2023)
- [AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal](https://mlsanity.com/paper/2601.11876/) (2026)

## Abstract (arXiv)

This paper presents a novel garbage pickup robot which operates on the grass. The robot is able to detect the garbage accurately and autonomously by using a deep neural network for garbage recognition. In addition, with the ground segmentation using a deep neural network, a novel navigation strategy is proposed to guide the robot to move around. With the garbage recognition and automatic navigation functions, the robot can clean garbage on the ground in places like parks or schools efficiently and autonomously. Experimental results show that the garbage recognition accuracy can reach as high as 95%, and even without path planning, the navigation strategy can reach almost the same cleaning efficiency with traditional methods. Thus, the proposed robot can serve as a good assistance to relieve dustman's physical labor on garbage cleaning tasks.