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

To address the challenges of high detection difficulty caused by small target size and complex background environments of traffic lights in urban road scenarios, this paper proposes a YOLO11n-based traffic light detection algorithm, named YOLO11n-PRE. Based on the baseline YOLO11n, the proposed algorithm first 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. Secondly, a P2 small object detection layer is introduced to fuse shallow high-resolution feature information, expanding the original 3-scale detection architecture to 4-scale detection, which significantly strengthens the network’s perception and positioning ability for micro-scale traffic lights. Finally, an ECA efficient channel attention module is embedded into the PANet feature fusion path, which enhances key feature response and suppresses redundant information via adaptive weight calibration in the channel dimension. Experimental results show that compared with the baseline YOLOv11n, YOLO11n-PRE achieves a 1.9 percentage point improvement in mAP@0.5, with precision and recall increased by 1.8 and 2.3 percentage points respectively, and the parameter count is only 2.8M, which fully meets the requirements of real-time and efficient detection.

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

Detecting small and distant objects in road traffic scenarios remains challenging owing to limited pixel resolution, cluttered backgrounds, and resource constraints on edge computing platforms. This work presents LCA-Net, a computationally efficient framework for small object detection that balances accuracy with model complexity. The framework incorporates three complementary designs: an Adaptive Deformable Downsampling Module (ADDM) that merges asymmetric and deformable convolution operations to improve spatial feature encoding while explicitly accounting for the parameter and computational cost of offset and modulation-mask prediction; a Cross-Scale Feature Fusion Pyramid (CSFFP) specifically engineered for minute objects, which augments multi-scale feature learning and enhances detection of far-field small targets; and a Lightweight Feature-Gated Detection Head (LFGDH) that employs channel–spatial attention to selectively emphasize informative features, thereby reducing both parameter count and computational cost. On Udacity, LCA-Net improves mAP@0.5 by 2.3 percentage points; on VisDrone2019, it improves mAP@0.5 by 1.7 percentage points. Across both benchmarks, the complete model reduces the parameter count by 25.58% and GFLOPs by 16.05% relative to YOLOv8-N. On the RTX A6000, LCA-Net-N reduces forward-pass latency from 1.82 to 1.63 ms, increases throughput from 549 to 613 FPS, and lowers peak GPU memory from 1180 to 1015 MiB. These results demonstrate 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.