# "ScatSpotter" -- A Dog Poop Detection Dataset

arXiv: https://arxiv.org/abs/2412.16473 · HTML: https://mlsanity.com/paper/2412.16473/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 60 references that are in the map.
- First posted on arXiv 21 Dec 2024.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [A Step Toward Quantifying Independently Reproducible Machine Learning Research](https://mlsanity.com/paper/1909.06674/) (2019)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes](https://mlsanity.com/paper/2106.02740/) (2021)
- [TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris](https://mlsanity.com/paper/2007.08097/) (2020)
- [TACO: Trash Annotations in Context for Litter Detection](https://mlsanity.com/paper/2003.06975/) (2020)
- [RTSeg: Real-time Semantic Segmentation Comparative Study](https://mlsanity.com/paper/1803.02758/) (2018)
- [What is Reproducibility in Artificial Intelligence and Machine Learning Research?](https://mlsanity.com/paper/2407.10239/) (2024)
- [Piece of CAKE: A Comprehensive Queue Management Solution for Home Gateways](https://mlsanity.com/paper/1804.07617/) (2018)
- [GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images](https://mlsanity.com/paper/2407.06337/) (2024)

## Abstract (arXiv)

Small, amorphous waste objects such as biological droppings and microtrash can be difficult to see, especially in cluttered scenes, yet they matter for environmental cleanliness, public health, and autonomous cleanup. We introduce "ScatSpotter": a new dataset of images annotated with polygons around dog feces, collected to train and study object detection and segmentation systems for small potentially camouflaged outdoor waste. We gathered data in mostly urban environments, using "before/after/negative" (BAN) protocol: for a given location, we capture an image with the object present, an image from the same viewpoint after removal, and a nearby negative scene that often contains visually similar confusers. Image collection began in 2020. This paper focuses on two dataset checkpoints from 2025 and 2024. The dataset contains over 9000 images and 6000 polygon annotations. Of the author-captured images we held out 691 for validation and used the rest to train. Via community participation we obtained a 121-image test set that, while small, is independent from author-collected images and provides some generalization confidence across photographers, devices, and locations. Due to its limited size, we report both validation and test results. We explore the difficulty of the dataset using off-the-shelf VIT, MaskRCNN, YOLO-v9, and DINO-v2 models. Zero-shot DINO performs poorly, indicating limited foundational-model coverage of this category. Tuned DINO is the best model with a box-level average precision of 0.69 on a 691-image validation set and 0.7 on the test set. These results establish strong baselines and quantify the remaining difficulty of detecting small, camouflaged waste objects. To support open access to models and data, we compare centralized and decentralized distribution mechanisms and discuss trade-offs for sharing scientific data. Code and project details are hosted on GitHub.