Due to the limited computing resources and storage capacity of edge detection devices, efficient detection algorithms are typically required to meet real-time and accuracy requirements. Existing detectors often require a large number of parameters and high computational power to improve accuracy, which reduces detection speed and performance on low-power devices. To reduce computational load and enhance detection performance on edge devices, we propose a lightweight drone target detection algorithm, LD-YOLOv10. Firstly, we design a novel lightweight feature extraction structure called RGELAN, which utilizes re-parameterized convolutions and the newly designed Conv-Tiny as the computational structure to reduce the computational burden of feature extraction. The AIFI module was introduced, utilizing its multi-head attention mechanism to enhance the expression of semantic information. We construct the DR-PAN Neck structure, which obtains weak features of small targets with minimal computational load. Wise-IoU and EIoU are combined as new bounding box regression loss functions to adjust the competition between anchor boxes of different quality and the sensitivity of anchor box aspect ratios, providing a more intelligent gradient allocation strategy. Extensive experiments on the VisdroneDET-2021 and UAVDT datasets show that LD-YOLOv10 reduces the number of parameters by 62.4% while achieving a slight increase in accuracy and has a faster detection speed compared to other lightweight algorithms. When deployed on the low-power NVIDIA Jetson Orin Nano device, LD-YOLOv10 achieves a detection speed of 25 FPS.

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.

With the rapid proliferation of drones across various domains, aerial target detection has become increasingly crucial. However, the targets in aerial images present challenges such as scale variation, small size, and density, leading to suboptimal performance of current detectors on aerial images. Based on the aforementioned challenges, we design an efficient aerial target detection algorithm called DMFF-YOLO. Specifically, to address the issues of small target size and scale variation, we design the DMFF neck structure, adding a small target detection head to tackle the small target size problem, using the DMC module to fuse different scale features for enriching detailed information, and employing the DSSFF module to construct a scale sequence space to solve the target scale variation problem. In the network backbone, we employ RFCBAMConv modules as downsampling layers, which interact with receptive-field features to mitigate the information disparity caused by positional changes and outperform traditional convolutional layers. Finally, we design the Soft-NMS-CIoU module to address the issue of suppressing adjacent boxes due to dense targets. On the VisDrone dataset, compared to the original algorithm, our method reduces the number of parameters by 31.1% while achieving an 11.7% improvement in mAP50. Extensive experiments on the VisDrone, DOTA, and UAVDT datasets demonstrate that the proposed algorithm performs well in aerial image detection tasks.

The accurate and rapid detection of traffic signs is crucial for intelligent transportation systems. Aiming at the problems that traffic signs have including more small targets in road scenes as well as misdetection, omission, and low recognition accuracy under the influence of fog, we propose a model for detecting traffic signs in foggy road scenes—YOLO-TSF. Firstly, we design the CCAM attention module and combine it with the idea of local–global residual learning thus proposing the LGFFM to enhance the model recognition capabilities in foggy weather. Secondly, we design MASFFHead by introducing the idea of ASFF to solve the feature loss problem of cross-scale fusion and perform a secondary extraction of small targets. Additionally, we design the NWD-CIoU by combining NWD and CIoU to solve the issue of inadequate learning capacity of IoU for diminutive target features. Finally, to address the dearth of foggy traffic signs datasets, we construct a new foggy traffic signs dataset, Foggy-TT100k. The experimental results show that the mAP@0.5, mAP@0.5:0.95, Precision, and F1-score of YOLO-TSF are improved by 8.8%, 7.8%, 7.1%, and 8.0%, respectively, compared with YOLOv8s, which proves its effectiveness in detecting small traffic signs in foggy scenes with visibility between 50 and 200 m.