Detecting Tear Gas Canisters With Limited Training Data
Human rights investigations often require triaging large volumes of open source data in order to find moments within image, or video that are relevant to a given investigation and warrant further inspection. Searching for images of tear gas usage online manually is laborious and time-consuming. In this paper, we focus on object detection models to facilitate discovery and identification of tear gas canisters for human rights monitors. For CNN based object detection to work, a large amount of training data is required, and prior to our work, a dataset of tear gas canisters did not exist. To achieve our objective, we benchmark methods for training object detectors using limited labelled data: we fine-tune different object detection models on the limited labelled data and compare performance to a few shot detector and augmentation strategies using synthetic data. We provide a dataset for evaluating and training tear gas canister detectors and show how such detectors can be deployed for a real world application such as investigating human rights violations. Our experiments show that fine-tuning state of the art detectors perform as well as the few shot detector, and including synthetic data can improve results.