Leveraging Orientation for Weakly Supervised Object Detection with Application to Firearm Localization
This addresses firearm detection for security applications, offering a weakly supervised method that outperforms fully supervised detectors, though it is incremental in combining weak supervision with orientation handling.
The paper tackles the problem of firearm detection, which is challenging due to variations in shape and the inefficiency of axis-aligned bounding boxes for thin objects like rifles, by proposing a weakly supervised orientation-aware object detection algorithm that trains on axis-aligned boxes but detects oriented boxes, achieving mAP scores of 88.3 on axis-aligned and 77.5 on oriented bounding boxes on a new dataset.
Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size, and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using AxisAligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate the effectiveness of the proposed algorithm for firearm detection.