Vehicle detection and tracking technology plays a crucial role in Intelligent Transportation Systems. However, due to factors such as complex scenarios, diverse scales, and occlusions, issues like false detections, missed detections, and identity switches frequently occur. To address these problems, this paper proposes a multi-object vehicle detection and tracking algorithm based on CDS-YOLOv8 and improved ByteTrack. For vehicle detection, the Context-Guided (CG) module is introduced during the downsampling process to enhance feature extraction capabilities in complex scenarios. The Dilated Reparam Block (DRB) is reconstructed to tackle multi-scale issues, and Soft-NMS replaces the traditional NMS to improve performance in densely populated vehicle scenarios. For vehicle tracking, the state vector and covariance matrix of the Kalman filter are improved to better handle the nonlinear movement of vehicles, and Gaussian Smoothed Interpolation (GSI) is introduced to fill in trajectory gaps caused by detection misses. Experiments conducted on the UA-DETRAC dataset show that the improved algorithm increases detection performance, with mAP@0.5 and mAP@0.5:0.95 improving by 9% and 8.8%, respectively. In terms of tracking performance, mMOTA improves by 6.7%. Additionally, comparative experiments with mainstream detection and two-stage tracking algorithms demonstrate the superior performance of the proposed algorithm.

Object detection is one of the core technologies for autonomous driving. Current road object detection mainly relies on visible light, which is prone to missed detections and false alarms in rainy, night-time, and foggy scenes. Multispectral object detection based on the fusion of RGB and infrared images can effectively address the challenges of complex and changing road scenes, improving the detection performance of current algorithms in complex scenarios. However, previous multispectral detection algorithms suffer from issues such as poor fusion of dual-mode information, poor detection performance for multi-scale objects, and inadequate utilization of semantic information. To address these challenges and enhance the detection performance in complex road scenes, this paper proposes a novel multispectral object detection algorithm called MRD-YOLO. In MRD-YOLO, we utilize interaction-based feature extraction to effectively fuse information and introduce the BIC-Fusion module with attention guidance to fuse different modal information. We also incorporate the SAConv module to improve the model’s detection performance for multi-scale objects and utilize the AIFI structure to enhance the utilization of semantic information. Finally, we conduct experiments on two major public datasets, FLIR_Aligned and M³FD. The experimental results demonstrate that compared to other algorithms, the proposed algorithm achieves superior detection performance in complex road scenes.

To address the issues of blurred edges and contours, insufficient extraction of low-frequency information, and unclear texture details in ancient murals, which lead to decreased ornamental value and limited research significance of the murals, this paper proposes a novel ancient mural super-resolution reconstruction method, based on an attention mechanism and a multi-level residual network, termed AM-ESRGAN. This network builds a module for Multi-Scale Dense Feature Fusion (MDFF) to adaptively fuse features at different levels for more complete structural information regarding the image. The deep feature extraction module is improved with a new Sim-RRDB module, which expands capacity without increasing complexity. Additionally, a Simple Parameter-Free Attention Module for Convolutional Neural Networks (SimAM) is introduced to address the issue of insufficient feature extraction in the nonlinear mapping process of image super-resolution reconstruction. A new feature refinement module (DEABlock) is added to extract image feature information without changing the resolution, thereby avoiding excessive loss of image information and ensuring richer generated details. The experimental results indicate that the proposed method improves PSNR/dB by 3.4738 dB, SSIM by 0.2060, MSE by 123.8436, and NIQE by 0.1651 at a $\times 4$ scale factor. At a $\times 2$ scale factor, PSNR/dB improves by 4.0280 dB, SSIM increases by 3.38%, MSE decreases by 62.2746, and NIQE reduces by 0.1242. Compared to mainstream models, the objective evaluation metrics of the reconstructed images achieve the best results, and the reconstructed ancient mural images exhibit more detailed textures and clearer edges.