Insects are abundant species on the earth, and the task of identification and identification of insects is complex and arduous. How to apply artificial intelligence technology and digital image processing methods to automatic identification of insect species is a hot issue in current research. In this paper, the problem of automatic detection and classification recognition of butterfly photographs is studied, and a method of bio-labeling suitable for butterfly classification is proposed. On the basis of YOLO algorithm, by synthesizing the results of YOLO models with different training mechanisms, a butterfly automatic detection and classification recognition algorithm based on YOLO algorithm is proposed. It greatly improves the generalization ability of YOLO algorithm and makes it have better ability to solve small sample problems. The experimental results show that the proposed annotation method and integrated YOLO algorithm have high accuracy and recognition rate in butterfly automatic detection and recognition.
This work aims to explore and identify tiny and seemingly unrelated perturbations of images in object detection that will lead to performance degradation. While tininess can naturally be defined using $L_p$ norms, we characterize the degree of "unrelatedness" of an object by the pixel distance between the occurred perturbation and the object. Triggering errors in prediction while satisfying two objectives can be formulated as a multi-objective optimization problem where we utilize genetic algorithms to guide the search. The result successfully demonstrates that (invisible) perturbations on the…
YOLO and SGBM Integration for Autonomous Tree Branch Detection and Depth Estimation in Radiata Pine Pruning Applications
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（惠灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
FA-YOLO: Research On Efficient Feature Selection YOLO Improved Algorithm Based On FMDS and AGMF Modules
National Innovation Center for Digital Fishery（国家数字渔业创新中心）；Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs（农业农村部水产动物与畜禽智慧养殖技术重点实验室）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）；College of Engineering, China Agricultural University（中国农业大学工学院）
PDT: Uav Target Detection Dataset for Pests and Diseases Tree
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算中心（国家超级计算济南中心））；Shandong Provincial Key Laboratory of Computer Networks（山东省计算机网络重点实验室）；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）；SHANDONG SCICOM Information and Economy Research Institute Co., Ltd.（山东科信信息经济研究院有限公司）；Peking University（北京大学）
Structural bolts are critical components used in different structural elements, such as beam-column connections and friction damping devices. The clamping force in structural bolts is highly influenced by the bolt rotation. Much of the existing vision-based research about bolt rotation estimation relies on traditional computer vision algorithms such as Hough Transform to assess static images of bolts. This requires careful image preprocessing, and it may not perform well in the situation of complicated bolt assemblies, or in the presence of surrounding objects and background noise, thus hinder…
Chiang Mai University（清迈大学）；Vidyasirimedhi Institute of Science & Technology（威迪雅西里梅迪科学技术研究所）；Medensy Co., Ltd.（Medensy有限公司）；Mahidol University（玛希隆大学）
In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO2) and pulse rate (PR) values into a health monitoring system-unfortunately, such a process trend to be an error in typing. Several studies attempted to detect the physiological value from the captured image using optical character recognition (OCR). However, the technology has limited availability with high cost. Thus, this study aimed to propose a novel framework called PACMAN (Pandemic Accelerated Human-Machine Collaboration) with a low-resource deep learning-based computer vision. …
In recent years, an enormous amount of fluorescence microscopy images were collected in high-throughput lab settings. Analyzing and extracting relevant information from all images in a short time is almost impossible. Detecting tiny individual cell compartments is one of many challenges faced by biologists. This paper aims at solving this problem by building an end-to-end process that employs methods from the deep learning field to automatically segment, detect and classify cell compartments of fluorescence microscopy images of yeast cells. With this intention we used Mask R-CNN to automatical…
A Low-Cost UAV Deep Learning Pipeline for Integrated Apple Disease Diagnosis,Freshness Assessment, and Fruit Detection
Department of Electronics and Communication Engineering, Heritage Institute of Technology（电子与通信工程系，遗产理工学院）；Department of Computer Science and Engineering (AI & ML), Heritage Institute of Technology（计算机科学与工程系（人工智能与机器学习），遗产理工学院）
University of Coimbra（科英布拉大学）；Universidade de Aveiro（阿威罗大学）；Centre for Research and Development in Mathematics and Applications（数学与应用研究发展中心）；Universidade do Minho（米尼奥大学）；Centro de Física das Universidades do Minho e do Porto（米尼奥大学与波尔图大学物理中心）；LIP（葡萄牙高能物理实验室）；Universitat de València（瓦伦西亚大学）；Observatori Astronòmic, Universitat de València（瓦伦西亚大学天文台）
We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the synthetic data generation and preparation tasks based on approximant waveform models used for the model training, validation and testing steps. Using this approach, we achieve mean average precision ($\text{mAP}_{[0.50]}$) values of 0.945 for a single class validation dataset and as…
A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Electrical and Computer Engineering, Kharazmi University（卡扎尔米大学电气与计算机工程学院）；Faculty of Mathematics and Computer Science, University of Bremen（不莱梅大学数学与计算机科学学院）；School of Computing, University of the Fraser Valley（弗雷泽大学计算机学院）
Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI
São Carlos School of Engineering, University of São Paulo（圣卡洛斯工程学院，圣保罗大学）；Department of Ecology and Evolutionary Biology, Federal University of São Carlos（生态与进化生物学系，巴西联邦圣卡洛斯大学）；Federal University of Tocantins（托坎蒂斯联邦大学）；Department of Biology, University of São Paulo（生物学系，圣保罗大学）；State University of Piauí, Deputado Jesualdo Cavalcanti Campus（皮亚伊州立大学，德putado Jesualdo Cavalcanti校区）
The use of mobiles phones when driving have been a major factor when it comes to road traffic incidents and the process of capturing such violations can be a laborious task. Advancements in both modern object detection frameworks and high-performance hardware has paved the way for a more automated approach when it comes to video surveillance. In this work, we propose a custom-trained state-of-the-art object detector to work with roadside cameras to capture driver phone usage without the need for human intervention. The proposed approach also addresses the issues caused by windscreen glare and …
Dual-Integrated Low-Latency Single-Lens Infrared Computational Imaging for Object Detection
MOE Key Laboratory of Advanced Micro-Structured Materials（教育部先进微结构材料重点实验室）；Institute of Precision Optical Engineering（精密光学工程研究院）；School of Physics Science and Engineering（物理科学与工程学院）；Shanghai Frontiers Science Center of Digital Optics（上海前沿科学中心数字光学中心）；School of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Shandong Normal University（山东师范大学）；Shandong Engineering Research Center for Multimodal Computing and Intelligent Decision Making（山东省多模态计算与智能决策中心）
Towards automatic smoke detector inspection: Recognition of the smoke detectors in industrial facilities and preparation for future drone integration
Brno University of Technology（布尔诺理工大学）；VSB - Technical University of Ostrava（俄斯特拉发技术大学）；University of Ostrava（俄斯特拉发大学）；Mendel University in Brno（布尔诺孟德尔大学）
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）
