Object detection as part of computer vision can be crucial for traffic management, emergency response, autonomous vehicles, and smart cities. Despite significant advances in object detection, detecting small objects in images captured by distant cameras remains challenging due to their size, distance from the camera, varied shapes, and cluttered backgrounds. To address these challenges, we propose Small Object Detection YOLOv8 (SOD-YOLOv8), a novel model specifically designed for scenarios involving numerous small objects. Inspired by Efficient Generalized Feature Pyramid Networks (GFPN), we enhance multi-path fusion within YOLOv8 to integrate features across different levels, preserving details from shallower layers and improving small object detection accuracy. Also, A fourth detection layer is added to leverage high-resolution spatial information effectively. The Efficient Multi-Scale Attention Module (EMA) in the C2f-EMA module enhances feature extraction by redistributing weights and prioritizing relevant features. We introduce Powerful-IoU (PIoU) as a replacement for CIoU, focusing on moderate-quality anchor boxes and adding a penalty based on differences between predicted and ground truth bounding box corners. This approach simplifies calculations, speeds up convergence, and enhances detection accuracy. SOD-YOLOv8 significantly improves small object detection, surpassing widely used models in various metrics, without substantially increasing computational cost or latency compared to YOLOv8s. Specifically, it increases recall from 40.1\% to 43.9\%, precision from 51.2\% to 53.9\%, $\text{mAP}_{0.5}$ from 40.6\% to 45.1\%, and $\text{mAP}_{0.5:0.95}$ from 24\% to 26.6\%. In dynamic real-world traffic scenes, SOD-YOLOv8 demonstrated notable improvements in diverse conditions, proving its reliability and effectiveness in detecting small objects even in challenging environments.
Salient Object Detection in Traffic Scene through the TSOD10K Dataset
College of Information Science and Engineering, Hunan Normal University（湖南师范大学信息科学与工程学院）；College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；National Engineering Research Center of Robot Visual Perception and Control Technology, School of Robotics, Hunan University（湖南大学机器人学院机器人视觉感知与控制技术国家工程研究中心）
Nankai University（南开大学）；ETH Zurich（苏黎世联邦理工学院）；University of Electronic Science and Technology of China（电子科技大学）
Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features collaborate in locating salient objects and discovering fine details, respectively. However, most efforts are devoted to low-level feature learning by fusing multi-scale features or enhancing boundary representations. High-level features, which although have long proven effective for many other tasks, yet have been barely studied for SOD. In this paper, we tap into this gap and show that enhancing high- level features is essential for SOD as well. To this end…
STENet: Superpixel Token Enhancing Network for RGB-D Salient Object Detection
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics（云南财经大学服务计算重点实验室）；Innovation Academy for Microsatellites of Chinese Academy of Science（中国科学院微卫星创新院）；Institute for Urban Governance, Shanghai University（上海大学城市治理研究院）
Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection
Department of Physics, J.K. Institute of Science（J.K.科学研究所物理系）；World Scientific University（世界科学出版社大学）；University of Intelligent Studies（智能研究大学）；School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）
Rapid Salient Object Detection with Difference Convolutional Neural Networks
College of Computer Science, Nankai University（南开大学计算机科学学院）；Center for Machine Vision and Signal Analysis (CMVS), University of Oulu（奥卢大学机器视觉与信号分析中心）；Intel Labs（英特尔实验室）；College of Electronic Science and Technology, NUDT（国防科技大学电子科学学院）
The High-Resolution Transformer (HRFormer) can maintain high-resolution representation and share global receptive fields. It is friendly towards salient object detection (SOD) in which the input and output have the same resolution. However, two critical problems need to be solved for two-modality SOD. One problem is two-modality fusion. The other problem is the HRFormer output's fusion. To address the first problem, a supplementary modality is injected into the primary modality by using global optimization and an attention mechanism to select and purify the modality at the input level. To solv…
SAMSOD: Rethinking SAM Optimization for RGB-T Salient Object Detection
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University（教育部智能计算与信号处理重点实验室，计算机科学与技术学院，安徽大学）
Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection
Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology（可信自主系统研究院和南方科技大学计算机科学与工程系）；Southern University of Science and Technology（南方科技大学）；Pengcheng Laboratory（鹏城实验室）；Pazhou Laboratory (Huangpu)（黄埔实验室）；Harbin Institute of Technology at Shenzhen（深圳哈尔滨工业大学）
Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
School of Information Engineering, Chang’an University（信息工程学院，长安大学）；School of Vehicle Mobility & College of AI, Tsinghua University（车辆运动学院与人工智能学院，清华大学）
Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition
Institute for Intelligent Systems（智能系统研究所）；Esslingen University of Applied Sciences（应用科学大学埃斯林根）；Department of Computer Science（计算机科学系）；University of Freiburg（弗赖堡大学）
Technical University of Munich（慕尼黑工业大学）；TUM Department of Civil, Geo and Environmental Engineering（慕尼黑工业大学土木工程、地质与环境工程系）
We present a new two-stage pipeline for predicting frames of traffic scenes where relevant objects can still reliably be detected. Using a recent video prediction network, we first generate a sequence of future frames based on past frames. A second network then enhances these frames in order to make them appear more realistic. This ensures the quality of the predicted frames to be sufficient to enable accurate detection of objects, which is especially important for autonomously driving cars. To verify this two-stage approach, we conducted experiments on the Cityscapes dataset. For enhancing, w…
Enhancing Traffic Incident Response through Sub-Second Temporal Localization with HybridMamba
Department of Computer Science, Iowa State University, Ames, IA, USA（计算机科学系，爱荷华州立大学）；Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA, USA（土木、建设与环境工程系，爱荷华州立大学）
Traffic sign detection is a vital task in the visual system of self-driving cars and the automated driving system. Recently, novel Transformer-based models have achieved encouraging results for various computer vision tasks. We still observed that vanilla ViT could not yield satisfactory results in traffic sign detection because the overall size of the datasets is very small and the class distribution of traffic signs is extremely unbalanced. To overcome this problem, a novel Pyramid Transformer with locality mechanisms is proposed in this paper. Specifically, Pyramid Transformer has several s…
YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multibranch Feature Interaction
Guangdong Key Laboratory of Intelligent Transportation System, School of intelligent systems engineering, Sun Yat-sen University（广东智能交通系统重点实验室，智能系统工程学院，中山大学）；Department of Architecture and Civil Engineering, Chalmers University of Technology（建筑与土木工程系，查尔姆斯理工大学）；School of Mechanical Engineering, Southeast University（机械工程学院，东南大学）；School of Computer Science and Engineering, Sun Yat-sen University（计算机科学与工程学院，中山大学）
