Orientation Aware Weapons Detection In Visual Data : A Benchmark Dataset

TL;DR

This paper introduces a new CNN-based method for detecting weapons with orientation awareness, utilizing a novel dataset of 6400 images, and demonstrates improved detection performance over existing methods.

Contribution

The paper presents a novel orientation-aware CNN architecture for weapon detection and provides a new annotated dataset with oriented bounding boxes for research.

Findings

Proposed model outperforms existing object detectors in weapon detection accuracy.

Introduced a new dataset with 6400 annotated weapon images and orientation information.

Demonstrated effectiveness of orientation-aware detection in complex scenarios.

Abstract

Automatic detection of weapons is significant for improving security and well being of individuals, nonetheless, it is a difficult task due to large variety of size, shape and appearance of weapons. View point variations and occlusion also are reasons which makes this task more difficult. Further, the current object detection algorithms process rectangular areas, however a slender and long rifle may really cover just a little portion of area and the rest may contain unessential details. To overcome these problem, we propose a CNN architecture for Orientation Aware Weapons Detection, which provides oriented bounding box with improved weapons detection performance. The proposed model provides orientation not only using angle as classification problem by dividing angle into eight classes but also angle as regression problem. For training our model for weapon detection a new dataset comprising of total 6400 weapons images is gathered from the web and then manually annotated with position oriented bounding boxes. Our dataset provides not only oriented bounding box as ground truth but also horizontal bounding box. We also provide our dataset in multiple formats of modern object detectors for further research in this area. The proposed model is evaluated on this dataset, and the comparative analysis with off-the shelf object detectors yields superior performance of proposed model, measured with standard evaluation strategies. The dataset and the model implementation are made publicly available at this link: https://bit.ly/2TyZICF.