Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3
University of Ostrava（奥斯特拉发大学）；Varroc Lighting Systems（瓦罗克照明系统公司）
Petr Hurtik, Vojtech Molek, Jan Hula, Marek Vajgl, Pavel Vlasanek, Tomas Nejezchleba
备注：
18 pages, 15 figures, submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (under review), Source code is available at https://gitlab.com/irafm-ai/poly-yolo
We present a new version of YOLO with better performance and extended with instance segmentation called Poly-YOLO. Poly-YOLO builds on the original ideas of YOLOv3 and removes two of its weaknesses: a large amount of rewritten labels and inefficient distribution of anchors. Poly-YOLO reduces the issues by aggregating features from a light SE-Darknet-53 backbone with a hypercolumn technique, using stairstep upsampling, and produces a single scale output with high resolution. In comparison with YOLOv3, Poly-YOLO has only 60% of its trainable parameters but improves mAP by a relative 40%. We also present Poly-YOLO lite with fewer parameters and a lower output resolution. It has the same precision as YOLOv3, but it is three times smaller and twice as fast, thus suitable for embedded devices. Finally, Poly-YOLO performs instance segmentation using bounding polygons. The network is trained to detect size-independent polygons defined on a polar grid. Vertices of each polygon are being predicted with their confidence, and therefore Poly-YOLO produces polygons with a varying number of vertices.
Melanoma is one of the most serious skin cancers that can occur in any part of the human skin. Early diagnosis of melanoma lesions will significantly increase their chances of being cured. Improving melanoma segmentation will help doctors or surgical robots remove the lesion more accurately from body parts. Recently, the learning-based segmentation methods achieved desired results in image segmentation compared to traditional algorithms. This study proposes a new approach to improve melanoma skin lesions detection and segmentation by defining a two-step pipeline based on deep learning models. …
YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation
Southern University of Science and Technology（南方科技大学）；the University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model
University of Liverpool（利物浦大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；JITRI Micro-Nano Automation Institute（JITRI微纳米自动化研究院）；Yunnan Normal University（云南师范大学）
Oak Ridge National Lab (ORNL)（橡树岭国家实验室）；St. Jude Children’s Hospital（圣裘德儿童医院）
Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the $z$-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this work, we introduce a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue. The proposed method combines the 2D YOLO detection method with a m…
Generalization vs. Specialization: Evaluating Segment Anything Model (SAM3) Zero-Shot Segmentation Against Fine-Tuned YOLO Detectors
Cornell University, Department of Biological and Environmental Engineering（康奈尔大学生物与环境工程系）；University of the Peloponnese, Department of Informatics and Telecommunications（希腊皮埃里亚大学信息与电信系）
Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development
Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation
Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；Technical University of Munich (TUM)（慕尼黑工业大学）；Aalto University（阿尔托大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）
Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology（密苏里科技大学机械与航空航天工程系）；Intelligent System Center, Missouri University of Science and Technology（密苏里科技大学智能系统中心）；Center for Biomedical Research, Missouri University of Science and Technology（密苏里科技大学生物医学研究中心）；Wound Care Department, Charles George Department of Veterans Affairs Medical Center（查尔斯·乔治退伍军人事务医疗中心伤口护理部）
With the advancements made in deep learning, computer vision problems like object detection and segmentation have seen a great improvement in performance. However, in many real-world applications such as autonomous driving vehicles, the risk associated with incorrect predictions of objects is very high. Standard deep learning models for object detection such as YOLO models are often overconfident in their predictions and do not take into account the uncertainty in predictions on out-of-distribution data. In this work, we propose an efficient and effective approach to model uncertainty in objec…
Lidar based 3D object detection and classification tasks are essential for automated driving(AD). A Lidar sensor can provide the 3D point coud data reconstruction of the surrounding environment. But the detection in 3D point cloud still needs a strong algorithmic challenge. This paper consists of three parts.(1)Lidar-camera calib. (2)YOLO, based detection and PointCloud extraction, (3) k-means based point cloud segmentation. In our research, Camera can capture the image to make the Real-time 2D Object Detection by using YOLO, I transfer the bounding box to node whose function is making 3d obje…
YOLO-Based Pipeline Monitoring in Challenging Visual Environments
Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa（电子电气与航海工程系（DITEN），热那亚大学）；Department of Bio-systems Engineering, Gyeongsang National University（生物系统工程系，全州国立大学）；Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences（机械工程与航海研究系，西挪威应用科学大学）
OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors
Department of Computer Science and Engineering（计算机科学与工程系）；School of IT and Engineering（信息与工程学院）；Indian Institute of Technology Kharagpur（印度理工学院Kharagpur分校）；Kazakh-British Technical University（哈萨克-英国技术大学）；Intelligent Devices Institute（智能设备研究所）；St. Petersburg Electrotechnical University "LETI"（圣彼得堡电力大学"LETI"）；Department of Automation and Control Processes（自动化与控制过程系）
