Using Cross-Domain Detection Loss to Infer Multi-Scale Information for Improved Tiny Head Tracking
Department of Electrical and Computer Engineering, University of Nebraska-Lincoln（内布拉斯加大学林肯分校电气与计算机工程系）；Department of Electrical and Computer Engineering, University of Maryland（马里兰大学电气与计算机工程系）；Department of Electrical and Computer Engineering, University of Arizona（亚利桑那大学电气与计算机工程系）
Jisu Kim, Alex Mattingly, Eung-Joo Lee, Benjamin S. Riggan
备注：
To appear at IEEE International Conference on Automatic Face and Gesture 2025 (FG2025)
Head detection and tracking are essential for downstream tasks, but current methods often require large computational budgets, which increase latencies and ties up resources (e.g., processors, memory, and bandwidth). To address this, we propose a framework to enhance tiny head detection and tracking by optimizing the balance between performance and efficiency. Our framework integrates (1) a cross-domain detection loss, (2) a multi-scale module, and (3) a small receptive field detection mechanism. These innovations enhance detection by bridging the gap between large and small detectors, capturing high-frequency details at multiple scales during training, and using filters with small receptive fields to detect tiny heads. Evaluations on the CroHD and CrowdHuman datasets show improved Multiple Object Tracking Accuracy (MOTA) and mean Average Precision (mAP), demonstrating the effectiveness of our approach in crowded scenes.
Central China Normal University（华中师范大学）；Northwest Normal University（西北师范大学）
Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections. However, multiple tiny defects on the PV panel surface and the high similarity between different defects make it challenging to {accurately identify and detect such defects}. This paper proposes an approach named Ghost convolution with BottleneckCSP and a tiny target prediction head incorporating YOLOv5 (GBH-YOLOv…
An Analysis of Kalman Filter based Object Tracking Methods for Fast-Moving Tiny Objects
McNeese State University（麦基内斯州立大学）；University of Louisiana at Lafayette（路易斯安那州立大学拉法叶分校）；Informatics Research Institute (IRI)（信息研究研究院）；Center for Advanced Computer Studies (CACS)（高级计算机研究中心）
High-Precision Transformer-Based Visual Servoing for Humanoid Robots in Aligning Tiny Objects
Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China（人形机器人研究院，精密机械与精密仪器系，中国科学技术大学）
Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
Existing head pose estimation (HPE) mainly focuses on single person with pre-detected frontal heads, which limits their applications in real complex scenarios with multi-persons. We argue that these single HPE methods are fragile and inefficient for Multi-Person Head Pose Estimation (MPHPE) since they rely on the separately trained face detector that cannot generalize well to full viewpoints, especially for heads with invisible face areas. In this paper, we focus on the full-range MPHPE problem, and propose a direct end-to-end simple baseline named DirectMHP. Due to the lack of datasets applic…
From Full Boards to Tiny Defects: Scale-Aware Tile Inference with Topology-Aware Merging for High-Resolution PCB Defect Detection
Dept. of Electrical and Computer Engineering（电气与计算机工程系）；Dept. of Marketing（市场营销系）；Centre of Real Time Computer Systems, Faculty of Informatics, Kaunas University of Technology（实时计算机系统中心，信息学院，凯纳斯技术大学）
Sri Lanka Institute of Information Technology（斯里兰卡信息技术学院）；Ulster University（阿尔斯特大学）
Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model for head-pose estimation.
Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones
University of Bologna（博洛尼亚大学）；ETH Zürich（苏黎世联邦理工学院）；Technology Innovation Institute（技术创新研究院）；Dalle Molle Institute for Artificial Intelligence（达勒·莫勒人工智能研究所）
Beijing University of Posts and Telecommunications（北京邮电大学）；Huazhong University of Science and Technology（华中科技大学）
For tiny obstacle discovery in a monocular image, edge is a fundamental visual element. Nevertheless, because of various reasons, e.g., noise and similar color distribution with background, it is still difficult to detect the edges of tiny obstacles at long distance. In this paper, we propose an obstacle-aware discovery method to recover the missing contours of these obstacles, which helps to obtain obstacle proposals as much as possible. First, by using visual cues in monocular images, several multi-layer regions are elaborately inferred to reveal the distances from the camera. Second, severa…
Convolutional neural networks (CNNs) are increasingly being used to automate segmentation of organs-at-risk in radiotherapy. Since large sets of highly curated data are scarce, we investigated how much data is required to train accurate and robust head and neck auto-segmentation models. For this, an established 3D CNN was trained from scratch with different sized datasets (25-1000 scans) to segment the brainstem, parotid glands and spinal cord in CTs. Additionally, we evaluated multiple ensemble techniques to improve the performance of these models. The segmentations improved with training set…
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（DarwinAI公司）
Object detection remains an active area of research in the field of computer vision, and considerable advances and successes has been achieved in this area through the design of deep convolutional neural networks for tackling object detection. Despite these successes, one of the biggest challenges to widespread deployment of such object detection networks on edge and mobile scenarios is the high computational and memory requirements. As such, there has been growing research interest in the design of efficient deep neural network architectures catered for edge and mobile usage. In this study, w…
