This research proposed an end-to-end object detection network based on Kolmogorov–Arnold Networks (KANs)-Detection Transformer (DETR). KANs block was introduced into encoder–decoder structure instead of the full connection layer to dynamically learn the activation function and improve the robustness and accuracy of the model. Experiments showed that the detection capability of KANs-DETR on multicategory object detection was better than that of HGNetv2 and Swin Transformer as backbone. Furthermore, in order to solve the problem of insensitivity to small objects, the Squeeze-and-Excitation module was applied for feature fusion and presented better performance. The KANs-DETR achieved high detection accuracy and efficiency in handling small objects in complex scenes, providing a new perspective for network optimization.

In this paper, an improved Transformer network is proposed, which reduces the overall computation of the network by nearly 50°/o while still maintaining good network performance while only retaining K and V values. Experiments show that with Swin Transformer as the backbone network, the improved method proposed in this paper can reduce the training time and testing time while maintaining high accuracy.

In this paper, we propose a lightweight mask detection algorithm and implement an intelligent vehicle system. The algorithm uses YOLOv5s as the backbone network, and at the same time incorporates the SE attention mechanism to optimize the timeliness, and is finally deployed on an intelligent vehicle system with BCM2711 as the control platform. Experiments prove that the algorithm proposed in this paper reduces the detection time by 30% while ensuring a higher MAP, which has certain value for promotion.