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Timely handgun detection is a crucial problem to improve public safety;nevertheless, the effectiveness of many surveillance systems still depends offinite human attention. Much of the previous research on handgun detection isbased on static image detectors, leaving aside valuable temporal informationthat could be used to improve object detection in videos. To improve theperformance of surveillance systems, a real-time temporal handgun detectionsystem should be built. Using Temporal Yolov5, an architecture based onQuasi-Recurrent Neural Networks, temporal information is extracted from videoto improve the results of handgun detection. Moreover, two publicly availabledatasets are proposed, labeled with hands, guns, and phones. One containing2199 static images to train static detectors, and another with 5960 frames ofvideos to train temporal modules. Additionally, we explore two temporal dataaugmentation techniques based on Mosaic and Mixup. The resulting systems arethree temporal architectures: one focused in reducing inference with amAP$_{50:95}$ of 55.9, another in having a good balance between inference andaccuracy with a mAP$_{50:95}$ of 59, and a last one specialized in accuracywith a mAP$_{50:95}$ of 60.2. Temporal Yolov5 achieves real-time detection inthe small and medium architectures. Moreover, it takes advantage of temporalfeatures contained in videos to perform better than Yolov5 in our temporaldataset, making TYolov5 suitable for real-world applications. The source codeis publicly available at https://github.com/MarioDuran/TYolov5.

TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video