Domain adaptive object detection (DAOD) aims to reduce performance degradation due to distribution shifts between training (source) and testing (target) datasets. However, owing to insufficient modeling of fine-grained features and limited instance-level alignment, existing approaches become less effective under complex environmental conditions, such as extreme weather conditions and object occlusion. We overcome these challenges by proposing JMT-YOLO, a novel joint multi-task learning framework for DAOD. JMT-YOLO is based on the Mean Teacher architecture. It introduces an auxiliary semantic segmentation task and a masking mechanism to enhance the modeling of foreground-background semantic boundaries in the target domain. Specifically, fine-grained feature degradation is reduced by introducing a multiscale context-aware segmentation head equipped with a dense connection structure to effectively capture local and global contextual features. A mask-consistency loss is formulated by applying random masking operations to target images, thereby guiding the network to learn structural context and improving instance-level feature alignment. Extensive experiments on three benchmark domain adaptation tasks (Pascal VOC to Clipart1k, Cityscapes to Foggy Cityscapes, and KITTI to Cityscapes) demonstrate that JMT-YOLO achieves detection performances of 46.9%, 57.3%, and 61.0%, respectively. These results demonstrate superior performance over existing state-of-the-art and baseline methods, thereby validating the effectiveness and generalization of JMT-YOLO.
Deep neural network shows excellent use in a lot of real-world tasks. One of the deep learning tasks is object detection. Well-annotated datasets will affect deep neural network accuracy. More data learned by deep neural networks will make the model more accurate. However, a well-annotated dataset is hard to find, especially in a specific domain. To overcome this, computer-generated data or virtual datasets are used. Researchers could generate many images with specific use cases also with its annotation. Research studies showed that virtual datasets could be used for object detection tasks. Ne…
Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
Domain adaptive object detection (DAOD) aims to alleviate transfer performance degradation caused by the cross-domain discrepancy. However, most existing DAOD methods are dominated by outdated and computationally intensive two-stage Faster R-CNN, which is not the first choice for industrial applications. In this paper, we propose a novel semi-supervised domain adaptive YOLO (SSDA-YOLO) based method to improve cross-domain detection performance by integrating the compact one-stage stronger detector YOLOv5 with domain adaptation. Specifically, we adapt the knowledge distillation framework with t…
Domain shift is a major challenge for object detectors to generalize well to real world applications. Emerging techniques of domain adaptation for two-stage detectors help to tackle this problem. However, two-stage detectors are not the first choice for industrial applications due to its long time consumption. In this paper, a novel Domain Adaptive YOLO (DA-YOLO) is proposed to improve cross-domain performance for one-stage detectors. Image level features alignment is used to strictly match for local features like texture, and loosely match for global features like illumination. Multi-scale in…
FogGuard: guarding YOLO against fog using perceptual loss
School of Computing and Information Science, University of Maine（计算与信息科学学院，缅因大学）；Department of Electrical and Computer Engineering, University of Maine（电气与计算机工程系，缅因大学）
While domain adaptation has been used to improve the performance of object detectors when the training and test data follow different distributions, previous work has mostly focused on two-stage detectors. This is because their use of region proposals makes it possible to perform local adaptation, which has been shown to significantly improve the adaptation effectiveness. Here, by contrast, we target single-stage architectures, which are better suited to resource-constrained detection than two-stage ones but do not provide region proposals. To nonetheless benefit from the strength of local ada…
The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many applications. This problem arises due to the difference between the distributions of source data used for training in comparison with target data used during realistic testing scenarios. In this paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO) framework that employs multiple domain adaptation paths and corresponding domain classifiers at different scales of the recently introduced YOLOv4 object detector to generate domain-invariant features. We train and test ou…
This survey paper specially analyzed computer vision-based object detection challenges and solutions by different techniques. We mainly highlighted object detection by three different trending strategies, i.e., 1) domain adaptive deep learning-based approaches (discrepancy-based, Adversarial-based, Reconstruction-based, Hybrid). We examined general as well as tiny object detection-related challenges and offered solutions by historical and comparative analysis. In part 2) we mainly focused on tiny object detection techniques (multi-scale feature learning, Data augmentation, Training strategy (T…
Fourier Domain Adaptation for Traffic Light Detection in Adverse Weather
Manipal Institute of Technology（曼尼帕尔理工学院）；Manipal Academy of Higher Education（曼尼帕尔高等教育学院）；Wells Fargo International Solutions Private Limited（富国国际解决方案私人有限公司）
In this paper we propose a method for improving pedestrian detection in the thermal domain using two stages: first, a generative data augmentation approach is used, then a domain adaptation method using generated data adapts an RGB pedestrian detector. Our model, based on the Least-Squares Generative Adversarial Network, is trained to synthesize realistic thermal versions of input RGB images which are then used to augment the limited amount of labeled thermal pedestrian images available for training. We apply our generative data augmentation strategy in order to adapt a pretrained YOLOv3 pedes…
Manipal Institute of Technology, Manipal Academy of Higher Education（马尼帕尔高等教育学院马尼帕尔理工学院）
The recent and rapid growth in Unmanned Aerial Vehicles (UAVs) deployment for various computer vision tasks has paved the path for numerous opportunities to make them more effective and valuable. Object detection in aerial images is challenging due to variations in appearance, pose, and scale. Autonomous aerial flight systems with their inherited limited memory and computational power demand accurate and computationally efficient detection algorithms for real-time applications. Our work shows the adaptation of the popular YOLOv4 framework for predicting the objects and their locations in aeria…
Towards benchmarking Western Bluebird detection in the wild
Universidad Nacional Autónoma de México（墨西哥国立自治大学）；Universidad Autónoma de Tlaxcala（特拉克斯卡拉自治大学）；The University of Texas at El Paso（德克萨斯大学埃尔帕索分校）；INAOE（国家天体物理、光学与电子学研究所）
Semi-Supervised Object Detection (SSOD) has been successful in improving the performance of both R-CNN series and anchor-free detectors. However, one-stage anchor-based detectors lack the structure to generate high-quality or flexible pseudo labels, leading to serious inconsistency problems in SSOD. In this paper, we propose the Efficient Teacher framework for scalable and effective one-stage anchor-based SSOD training, consisting of Dense Detector, Pseudo Label Assigner, and Epoch Adaptor. Dense Detector is a baseline model that extends RetinaNet with dense sampling techniques inspired by YOL…
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）
InfantFace: Detecting infant faces in neonatal clinical environments
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（牛津大学生物医学工程研究所、工程科学系）；Department of Paediatrics, University of Oxford（牛津大学儿科系）；Universidad San Francisco de Quito USFQ, Colegio de Ciencias Biologicas y Ambientales（奎托大学圣弗朗西斯科德奎托大学，生物科学与环境学院）
University of Canterbury（坎特伯雷大学）；HIT Lab NZ（新西兰HIT实验室）
With the emergence of onboard vision processing for areas such as the internet of things (IoT), edge computing and autonomous robots, there is increasing demand for computationally efficient convolutional neural network (CNN) models to perform real-time object detection on resource constraints hardware devices. Tiny-YOLO is generally considered as one of the faster object detectors for low-end devices and is the basis for our work. Our experiments on this network have shown that Tiny-YOLO can achieve 0.14 frames per second(FPS) on the Raspberry Pi 3 B, which is too slow for soccer playing auto…
XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling
Department of Research Computing（研究计算系）；University of Houston（休斯敦大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Engineering Technology（工程科技系）
Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment
School of Artificial Intelligence, China University of Mining and Technology (Beijing)（中国矿业大学（北京）人工智能学院）；University of British Columbia（不列颠哥伦比亚大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Northwestern University（西北大学）
Person search generally involves three important parts: person detection, feature extraction and identity comparison. However, person search integrating detection, extraction and comparison has the following drawbacks. Firstly, the accuracy of detection will affect the accuracy of comparison. Secondly, it is difficult to achieve real-time in real-world applications. To solve these problems, we propose a Multi-task Joint Framework for real-time person search (MJF), which optimizes the person detection, feature extraction and identity comparison respectively. For the person detection module, we …
Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection
LAAS-CNRS, Universite de Toulouse, CNRS（法国国家科学研究中心图卢兹分部LAAS、图卢兹大学、法国国家科学研究中心）；Department of Electrical Engineering and Automation at Aalto University（阿尔托大学电气工程与自动化系）
Thomas Lord Department of Computer Science, University of Southern California（汤姆·劳德计算机科学系，南加州大学）；Department of Computer Science, American International University-Bangladesh（计算机科学系，美国国际大学-孟加拉国）；Faculty of Information Science and Technology, Multimedia University（信息科学与技术学院，多媒体大学）
