A fine-grained detection algorithm for identity and actions of weapon-holding targets in public safety

Fine-grained detection of threat targets in public security can provide more precise and detailed security information.Compared with general security systems, it can achieve faster and more accurate responses, which is of great significance for ensuring public security. In the face of the problems of low detection accuracy, inaccurate classification, and complex scene in fine-grained threat detection, we propose a fine-grained threat detection model based on the improved RealTime Detection Transformers (RT-DETR) algorithm.First, we built a high-performance backbone network called the Dilated Feature Aggregation Network to efficiently extract multiscale features. We used dilated convolutions for spatial feature aggregation and reparameterized them as large kernel convolutions to efficiently capture fine-grained target features. Secondly, a new feature encoder was developed to enable the model to better capture local and global features in complex real-world scenarios.Meanwhile, we propose a new feature fusion module DyContext Fusion Module, which improves the efficiency and fine-grained feature representation of feature fusion by dynamic sampling, context-guided features, and convolutional fusion. Finally, we utilized Adaptive Classification Loss to balance positive and negative samples, increased the inter-class diversity, and improved the model's classification accuracy. We have built a public safety dataset and conducted comparative experiments with state-of-the-art algorithms that are publicly available to verify the effectiveness of the proposed improvement method. We also tested the performance of this method in terms of fine-grained and action detection on the Stanford 40 actions dataset, and the results showed that its performance was good. This proves the possibility of using this method for fine-grained detection of public security threat targets. The relevant code is released at https://github.com/ZIXUAN98/FG-DETR.