Triple-attention enhanced and RepViT-driven LiDAR 3D object detection for complex traffic scenarios

Ecf3dmot: enhanced centerpoint framework for 3D object detection and tracking with LiDAR

Fadet: a fusion-aware 3D detection network with cascaded feature enhancement for small object detection in autonomous driving

Improved YOLO11n for traffic light small object detection

A YOLO11n-based traffic light detection algorithm, named YOLO11n-PRE, which replaces the original C3k2 module in the backbone network with the C3k2-RCB module, which enhances deep feature extraction capability while maintaining lightweight via efficient residual connection and feature recalibration mechanism.

LCA-Net: A Lightweight Network for Small Object Detection in Road Traffic Scenes

LCA-Net is presented, a computationally efficient framework for small object detection that balances accuracy with model complexity that demonstrates a favorable accuracy–efficiency trade-off for real-time traffic perception.

SOD-MSC: Multi-Scale Context-Focused Detection for Small Traffic Objects in Aerial Images

The detection of small objects in aerial traffic scenes is vital for advancing intelligent transportation systems (ITS), yet traditional methods often suffer from scale variation, background complexity, and insufficient context utilization. To address these challenges, we propose SOD-MSC, a YOLOv8-based small object detector designed for remote sensing images. SOD-MSC integrates a global multi-scale (GMS) module to enhance global perception and multi-scale fusion, a lightweight fusion feature pyramid network (LFFPN) to strengthen spatial correlations and suppress background noise, and an adaptive RT-DETR-based detection head with Inner_SIoU loss to improve localization accuracy and convergence. Experiments on VisDrone2019-DET and HazyDet demonstrate that SOD-MSC achieves 39.9% and 49% on mAP@0.5, with only 5.22 M parameters and 11.2 GFLOPs, surpassing several baselines and state-of-the-art models in balancing accuracy and efficiency. These results highlight the effectiveness of SOD-MSC for dense small object detection in complex traffic environments, with promising applications in congestion monitoring, vehicle counting, and related ITS tasks.

MVXCC-NET: Cross-modal 3D detection of occluded objects based on dual-path information complementation and regional weight modeling

Surrounding scene awareness is a core component of self-driving techniques, and the 3D detection accuracy for occluded objects directly determines the system’s scenario adaptability and driving safety. To address the core problem of inter-object occlusions in traffic scenes that lead to reduced 3D detection accuracy and inadequate feature encoding, this paper presents MVXCC-NET, a cross-modal 3D detection network for occluded objects based on dual-path information complementation and regional weight modeling. The presented method adopts an image point cloud fusion framework with a dual branch design. In the image branch, a joint weighting strategy is introduced to progressively enhance image features and strengthen the visible regions of occluded objects. In the 3D point cloud path, a coordinate aware sparse region feature enhancement strategy is introduced to encode point cloud spatial coordinate information explicitly and perform feature weighting, thereby improving the geometric structure perception and spatial modeling ability of LiDAR for sparse point clouds in occluded areas, reducing the loss of geometric features caused by occlusion, and maintaining accurate 3D localization. Meanwhile, through multi-scale feature output and a cross modal deep fusion strategy, the proposed method improves the utilization efficiency of fused features, allowing visual semantic information and spatial geometric information to support each other. Experimental results on the KITTI occluded object dataset show that MVXCC-NET achieves detection accuracies of 55.3%, 72.6%, and 69.1% for pedestrians, cars, and cyclists, respectively. Compared with MVX-Net, POINTPILLARS, POINTRCNN, and SECOND, the mean accuracy is improved by 6.5%, 7.8%, 4.8%, and 14.2%, respectively.