This study investigates the domain generalization capabilities of three state-of-the-art object detection models - YOLOv8s, RT-DETR, and YOLO-NAS - within the unique driving environment of Kazakhstan. Utilizing the newly constructed ROAD-Almaty dataset, which encompasses diverse weather, lighting, and traffic conditions, we evaluated the models' performance without any retraining. Quantitative analysis revealed that RT-DETR achieved an average F1-score of 0.672 at IoU=0.5, outperforming YOLOv8s (0.458) and YOLO-NAS (0.526) by approximately 46% and 27%, respectively. Additionally, all models exhibited significant performance declines at higher IoU thresholds (e.g., a drop of approximately 20% when increasing IoU from 0.5 to 0.75) and under challenging environmental conditions, such as heavy snowfall and low-light scenarios. These findings underscore the necessity for geographically diverse training datasets and the implementation of specialized domain adaptation techniques to enhance the reliability of autonomous vehicle detection systems globally. This research contributes to the understanding of domain generalization challenges in autonomous driving, particularly in underrepresented regions.
RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity
Cornell University（康奈尔大学）；Kansas State University（堪萨斯州立大学）
LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection
Baidu Inc.（百度公司）；The University of Adelaide（阿德莱德大学）；Beihang University（北京航空航天大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Australian National University（澳大利亚国立大学）
Although the YOLOv2 method is extremely fast on object detection, its detection accuracy is restricted due to the low performance of its backbone network and the underutilization of multi-scale region features. Therefore, a dense connection (DC) and spatial pyramid pooling (SPP) based YOLO (DC-SPP-YOLO) method for ameliorating the object detection accuracy of YOLOv2 is proposed in this paper. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an impro…
Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 Mobility 学院）
Object Detection for Medical Image Analysis: Insights from the RT-DETR Model
University of California, Los Angeles（加州大学洛杉矶分校）；Duke University（杜克大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；University of Rochester（罗切斯特大学）；Northeastern University（东北大学）；Texas Tech University（德克萨斯理工大学）
Detection of On-Ground Chestnuts Using Artificial Intelligence Toward Automated Picking
School of Automation Engineering, University of Electronic Science；Department of Biosystems \& Agricultural Engineering, Michigan State University, East Lansing, MI 48824, United States
Butter: Frequency Consistency and Hierarchical Fusion for Autonomous Driving Object Detection
Tsinghua University（清华大学）；University of Chinese Academy of Sciences（中国科学院大学）；Southeast University（东南大学）；University of Liverpool（利物浦大学）；Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa
Bayero University（巴耶罗大学）；Alpen-Adria-Universität Klagenfurt（克拉根福阿尔卑斯-亚得里亚大学）；Gombe State University（贡贝州立大学）；Aliko Dangote University of Science and Technology（阿里科·丹格特科技大学）；Ahmadu Bello University（艾哈迈杜·贝洛大学）；Federal University Dutse（杜塞联邦大学）
OV-DEIM: Real-time DETR-Style Open-Vocabulary Object Detection with GridSynthetic Augmentation
Intellindust AI Lab（Intellindust AI实验室）；Guangdong Laboratory of Artificial Intelligence（广东人工智能实验室）；College of Computer Science（计算机科学学院）；Software Engineering, Shenzhen University, China（软件工程，深圳大学，中国）；School of Information Technology, Carleton University, Canada（信息科技学院，卡尔顿大学，加拿大）；Institute for Research in Biomedicine, Bellinzona, Switzerland（生物医学研究 institute，贝尔林扎，瑞士）
USU-Corn-WeedDB: A UAV RGB Image Dataset for Multi-Species Weed Detection in Forage Corn
Department of Applied Sciences, Technology, and Education, Utah State University（应用科学、技术和教育系，犹他州立大学）；Department of Plants, Soils & Climate, Utah State University（植物、土壤与气候系，犹他州立大学）；Department of Civil and Environmental Engineering, Utah State University（土木与环境工程系，犹他州立大学）
