Conditioned Cooperative training for semi-supervised weapon detection

Abstract

Violent assaults and homicides occur daily, and the number of victims of mass shootings increases every year. However, this number can be reduced with the help of Closed Circuit Television (CCTV) and weapon detection models, as generic object detectors have become increasingly accurate with more data for training. We present a new semi-supervised learning methodology based on conditioned cooperative student–teacher training with optimal pseudo-label generation using a novel confidence threshold search method and improving both models by conditional knowledge transfer. Furthermore, a novel firearms image dataset of 458,599 images was collected using Instagram hashtags to evaluate our approach and compare the improvements obtained using a specific unsupervised dataset instead of a general one such as ImageNet. We compared our methodology with supervised, semi-supervised and self-supervised learning techniques, outperforming approaches such as YOLOv5 m (up to ＋19.86), YOLOv5l (up to ＋6.52) Unbiased Teacher (up to ＋10.5 AP), DETReg (up to ＋2.8 AP) and UP-DETR (up to ＋1.22 AP).