Small object detection has important application value in the fields of autonomous driving and drone scene analysis. As one of the most advanced object detection algorithms, YOLOv3 suffers some challenges when detecting small objects, such as the problem of detection failure of small objects and occluded objects. To solve these problems, an improved YOLOv3 algorithm for small object detection is proposed. In the proposed method, the dilated convolutions mish (DCM) module is introduced into the backbone network of YOLOv3 to improve the feature expression ability by fusing the feature maps of different receptive fields. In the neck network of YOLOv3, the convolutional block attention module (CBAM) and multi-level fusion module are introduced to select the important information for small object detection in the shallow network, suppress the uncritical information, and use the fusion module to fuse the feature maps of different scales, so as to improve the detection accuracy of the algorithm. In addition, the Soft-NMS and Complete-IoU (CloU) strategies are applied to candidate frame screening, which improves the accuracy of the algorithm for the detection of occluded objects. The ablation experiment of the MS COCO2017 object detection task proves the effectiveness of several modules introduced in this paper for small object detection. The experimental results on the MS COCO2017, VOC2007, and VOC2012 datasets show that the Average Precision (AP) of this method is 16.5%, 8.71%, and 9.68% higher than that of YOLOv3, respectively.
Advanced Object Detection and Pose Estimation with Hybrid Task Cascade and High-Resolution Networks
California Institute of Technology（加州理工学院）；University of Michigan（密歇根大学）；University of Washington（华盛顿大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Northern Arizona University（北亚利桑那大学）
Myongji University（明知大学）；Chonnam University（全南大学）；HCMC University of Technology and Education（胡志明市技术师范大学）
A unified system integrating a compact object detector and a surrounding environmental condition classifier for enhancing the robustness of object detection scheme in advanced driver assistance systems (ADAS) is proposed in this paper. ADAS are invented to improve traffic safety and effectiveness in autonomous driving systems where object detection plays an extremely important role. However, modern object detectors integrated in ADAS are still unstable due to high latency and the variation of the environmental contexts in the deployment phase. Our system is proposed to address the aforemention…
National University of Ireland Galway（爱尔兰国立高威大学）；XPERI Corporation（XPERI公司）
Object detection in thermal infrared spectrum provides more reliable data source in low-lighting conditions and different weather conditions, as it is useful both in-cabin and outside for pedestrian, animal, and vehicular detection as well as for detecting street-signs & lighting poles. This paper is about exploring and adapting state-of-the-art object detection and classifier framework on thermal vision with seven distinct classes for advanced driver-assistance systems (ADAS). The trained network variants on public datasets are validated on test data with three different test approaches which…
In present object detection systems, the deep convolutional neural networks (CNNs) are utilized to predict bounding boxes of object candidates, and have gained performance advantages over the traditional region proposal methods. However, existing deep CNN methods assume the object bounds to be four independent variables, which could be regressed by the $\ell_2$ loss separately. Such an oversimplified assumption is contrary to the well-received observation, that those variables are correlated, resulting to less accurate localization. To address the issue, we firstly introduce a novel Intersecti…
Advanced Feature Manipulation for Enhanced Change Detection Leveraging Natural Language Models
Texas A&M University（德克萨斯农工大学）；Columbia University（哥伦比亚大学）；Miami University（迈阿密大学）；The University of Chicago（芝加哥大学）；The Kyoto College of Graduate Studies for Informatics（京都信息学研究生院）；New York University（纽约大学）
Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime
PT Mitra Solusi Telematika；Ingram School of Engineering（英格拉姆工程学院）；Texas State University（德克萨斯州立大学）；Department of Civil Engineering（土木工程系）；Rowan University（罗文大学）；Department of Geography and Environmental Studies（地理与环境研究系）
Institute of Advanced Research in Artificial Intelligence (IARAI)（高级人工智能研究所（IARAI））；Wuhan University（武汉大学）；Xidian University（西安电子科技大学）；Technical University of Munich（慕尼黑工业大学）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；Chengdu University of Technology（成都理工大学）
The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence (IARAI) are presented here. The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally. The 2022 L4S aims to foster interdisciplinary research on recent developments in deep learning (DL) models for the semantic segmentation task using satellite imagery. In the past few years, DL-based models have achieved performance that meets expectations on image interpretati…
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver Assistance Systems
Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University（广东省智能交通系统重点实验室，智能系统工程学院，中山大学）；Intelligent Transportation Systems Research Center, Wuhan University of Technology（智能交通系统研究中心，武汉理工大学）；State Key Laboratory of Automotive Simulation and Control, Jilin University（汽车模拟与控制国家重点实验室，吉林大学）；School of Transportation, Southeast University（交通学院，东南大学）；Department of Civil and Environmental Engineering, University of Alberta（土木与环境工程系，阿尔伯塔大学）
Advanced Gesture Recognition for Autism Spectrum Disorder Detection: Integrating YOLOv7, Video Augmentation, and VideoMAE for Naturalistic Video Analysis
Indian Institute of Information Technology, Allahabad（阿拉哈巴德印度信息技术学院）
MKSNet: Advanced Small Object Detection in Remote Sensing Imagery with Multi-Kernel and Dual Attention Mechanisms
School of Computer Science, Beijing Institute of Technology, Beijing, China（计算机科学学院，北京理工大学，北京）；Tangshan Research Institute, Beijing Institute of Technology, Tangshan, China（唐山研究所，北京理工大学，唐山）；Shanghai Institute of Satellite Engineering, Shanghai, China（上海卫星工程研究所，上海）
Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations
Dr. Ing. h.c. F. Porsche AG（保时捷股份公司）；Robert Bosch GmbH（罗伯特·博世有限公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；NEC Laboratories Europe GmbH（NEC欧洲实验室有限公司）；Leibniz University Hannover（汉诺威莱布尼茨大学）
In recent years, computer vision algorithms have become more powerful. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e.g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems …
ShrinkBox: Backdoor Attack on Object Detection to Disrupt Collision Avoidance in Machine Learning-based Advanced Driver Assistance Systems
eBRAIN Lab, New York University Abu Dhabi (NYUAD), UAE（eBRAIN实验室，纽约大学阿布扎赫尔分校（NYUAD），阿联酋）；AI and Digital Science Research Center, Technology Innovation Institute (TII), Abu Dhabi, UAE（人工智能与数字科学研究中心，技术创新研究所（TII），阿布扎赫尔，阿联酋）
Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
Computer vision is developing rapidly with the support of deep learning techniques. This thesis proposes an advanced vehicle-detection model based on an improvement to classical convolutional neural networks. The advanced model was applied against a vehicle detection benchmark and was built to detect on-road objects. First, we propose a high-level architecture for our advanced model, which utilizes different state-of-the-art deep learning techniques. Then, we utilize the residual neural networks and region proposal network to achieve competitive performance according to the vehicle detection b…
Duke University（杜克大学）；University of Louisville（路易斯维尔大学）；University of Missouri（密苏里大学）；Chemring Sensors and Electronic Systems (CSES)（Chemring传感器与电子系统公司（CSES））
In this paper we consider the development of algorithms for the automatic detection of buried threats using ground penetrating radar (GPR) measurements. GPR is one of the most studied and successful modalities for automatic buried threat detection (BTD), and a large variety of BTD algorithms have been proposed for it. Despite this, large-scale comparisons of GPR-based BTD algorithms are rare in the literature. In this work we report the results of a multi-institutional effort to develop advanced buried threat detection algorithms for a real-world GPR BTD system. The effort involved five instit…
University of Dayton（代顿大学）；Deep Lens Inc（Deep Lens公司）
Deep Learning (DL) approaches have been providing state-of-the-art performance in different modalities in the field of medical imagining including Digital Pathology Image Analysis (DPIA). Out of many different DL approaches, Deep Convolutional Neural Network (DCNN) technique provides superior performance for classification, segmentation, and detection tasks. Most of the task in DPIA problems are somehow possible to solve with classification, segmentation, and detection approaches. In addition, sometimes pre and post-processing methods are applied for solving some specific type of problems. Rec…
Haze removal is an extremely challenging task, and object detection in the hazy environment has recently gained much attention due to the popularity of autonomous driving and traffic surveillance. In this work, the authors propose a multiple linear regression haze removal model based on a widely adopted dehazing algorithm named Dark Channel Prior. Training this model with a synthetic hazy dataset, the proposed model can reduce the unanticipated deviations generated from the rough estimations of transmission map and atmospheric light in Dark Channel Prior. To increase object detection accuracy …
Sparse modeling is one of the efficient techniques for imaging that allows recovering lost information. In this paper, we present a novel iterative phase-retrieval algorithm using a sparse representation of the object amplitude and phase. The algorithm is derived in terms of a constrained maximum likelihood, where the wave field reconstruction is performed using a number of noisy intensity-only observations with a zero-mean additive Gaussian noise. The developed algorithm enables the optimal solution for the object wave field reconstruction. Our goal is an improvement of the reconstruction qua…
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection
University Center Salhi Ahmed（萨希·艾哈迈德大学中心）；University of Medea（迈代阿大学）；University of Dubai（迪拜大学）；Ecole Nationale Polytechnique（国立理工学院）；Texas A&M University（德克萨斯农工大学）；Northumbria University（诺森比亚大学）；Qatar University（卡塔尔大学）
Technische Universität Berlin（柏林工业大学）；Bundesanstalt für Geowissenschaften und Rohstoffe（德国联邦地球科学与自然资源研究所）
Subsurface tile drainage pipes provide agronomic, economic and environmental benefits. By lowering the water table of wet soils, they improve the aeration of plant roots and ultimately increase the productivity of farmland. They do however also provide an entryway of agrochemicals into subsurface water bodies and increase nutrition loss in soils. For maintenance and infrastructural development, accurate maps of tile drainage pipe locations and drained agricultural land are needed. However, these maps are often outdated or not present. Different remote sensing (RS) image processing techniques h…
Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms
Islamic Azad University（伊斯兰阿扎德大学）；University of Tehran（德黑兰大学）
University of California Santa Barbara（加州大学圣巴巴拉分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Cancer Center at Illinois（伊利诺伊癌症中心）
Purpose: To objectively assess new medical imaging technologies via computer-simulations, it is important to account for the variability in the ensemble of objects to be imaged. This source of variability can be described by stochastic object models (SOMs). It is generally desirable to establish SOMs from experimental imaging measurements acquired by use of a well-characterized imaging system, but this task has remained challenging. Approach: A generative adversarial network (GAN)-based method that employs AmbientGANs with modern progressive or multiresolution training approaches is proposed. …
