  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.
