Currently, object detection and recognition technologies are rapidly developing. Criminal activities in public places are becoming increasingly diverse, and they pose significant risks and challenges to social governance. We design a YOLOv10-EN detection network to satisfy practical application requirements for public security. In the BackBone and Neck networks of the model, a multiscale convolution module MSConv is designed to capture the spatial features of multiple scales in various receptive fields, thus enhancing the adaptability of the model to the targets. In the output PSA module of the BackBone network, a coordinate attention mechanism is introduced to aggregate the horizontal and vertical feature information via one-dimensional global pooling, capture long-distance dependencies, enhance global semantic information, and form a direction-aware global representation. Simultaneously, the coordinate attention mechanism preserves the positional information of the pooling operation results on the representation direction vector for feature encoding, thus achieving precise target modeling, improving the localization precision, and reducing the model size and computational cost. In the Bottleneck of the C2f module, residual connection and multiscale convolution mechanism are introduced. The jump structure of residual connection is employed to optimize the information flow transmission, alleviate gradient vanishing and explosion, and achieve efficient feature fusion. The multiscale convolution mechanism is used to capture different receptive field feature information to futher enhance the multiscale adaptation and feature expression of the network. During model training, the EIOU loss function is introduced to improve the precision and convergence speed of the bounding box regression. The experimental results show that the YOLOv10-EN detection network achieves varying degrees of improvement in terms of four key metrics: the F1 score, mAP@0.5, model parameter size, and FLOPs. Compared with the YOLOv10n baseline model, the F1 score and mAP@0.5 of the proposed model increase by 6.25% and 6.46%, respectively, whereas the model parameter size and computational complexity decrease by 20.45% and 22.97%, respectively. The experimental results show that the YOLOv10-EN network achieves a more precise detection while being lightweight, thus meeting the need for portable deployment on edge mobile devices.