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ROI Image Encryption using yolo and Chaotic Systems

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第7期12卷 466-474页

作者： Kang, Sung Won Choi, Un Sook Pukyong Natl Univ Dept Informat Secur Busan South Korea Tongmyong Univ Sch Artificial Intelligence Busan South Korea

In this paper, we design a cellular automata (CA)-based ROI (region of interest) image encryption system that can effectively reduce computational cost and maintain an appropriate level of security. The proposed image encryption system obtains a cryptographic image through three steps. First, a region of interest with high importance is extracted from the entire image using deep learning. We use the yolo (You Only Look Once) algorithm to extract the ROI from a given original image. Next, the detected ROI is encrypted using the Chen system, a chaotic-based function with high security. Finally, the execution time is effectively reduced by encrypting the entire image using a hardware-friendly CA. The safety of the proposed encryption system is verified through various statistical experiment results and analyses.

关键词： Image encryption cellular automata yolo algorithm deep learning Chen system region of interest

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Object Detection using Learning algorithm and IoT

Object Detection using Learning Algorithm and IoT

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： SenthilPandi, S. Mithun, D. Naresh Kumar, A. Paulraj, D. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Chennai India

ISBN: (纸本)9798350305708

To reduce accidents and safeguard the lives of drivers, it is crucial to ensure road safety. Helmets are essential for preventing brain injuries during car accidents and must be worn properly. In this research, we offer a Helmets Detection device for Road Safety that makes use of IoT (Internet of Things) and the cutting-edge object detection algorithms yolo (You Only Look Once).The proposed system makes use of the Raspberry Pi 3 and the IoT (Internet of Things) to gather real-time information from the camera. If the driver is wearing a helmet, the camera will gather an image of the helmet and if the ignition is successful, the engine will start. If the driver is not wears a helmet, our camera will capture an image and the engine fails to ignite. After being submitted into a reliable CNN (Convolutional Neural Network) model that was trained on an extensive collection of annotated helmet photos, the gathered data is then used. On a real-world dataset, we analysed this, and we were able to reach a high detection reliability of over 95%. The outcomes show that the suggested strategy is beneficial in enhancing road by recognising motorcyclists who neglect to put on helmets, accidents and injuries can be avoided. This information is sent to a central computer, where the yolo technique is used to locate and identify motorcyclists' helmets from collected photos. © 2023 IEEE.

关键词： Convolutional Neural Networks (CNN) IoT (Internet of Things) Raspberry Pi 3 Real-time detection Safety Helmet Detector yolo algorithm

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Bird Droppings Defects Detection in Photovoltaic Modules Based on CA-yolov5

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PROCESSES 2024年第6期12卷 1248页

作者： Liu, Linjun Li, Qiong Liao, Xu Wu, Wenbao Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Peoples R China China Power Construct Grp Jiangxi Power Construct Nanchang 330001 Peoples R China

Due to the characteristics of different forms and small bird droppings-related defects in photovoltaic modules, problems of missing detection, wrong detection, and low detection accuracy often exist in the bird droppings covering the detection of photovoltaic modules. In this paper, a defect identification method based on the improved Coordinate Attention-You Only Look Once (CA-yolov5) network is proposed. Firstly, a layer of the Coordinate Attention module is added between the Backbone and Neck network, which takes into account channel and location information to enhance the feature extraction ability of the network model. Secondly, the small target detection layer is added to fuse different feature information from shallow networks and deep networks so as to improve the multi-scale feature detection ability of the network structure and effectively improve the detection effect of small targets. Finally, the experimental results show that, compared with the original algorithm, the proposed improved algorithm based on yolov5s has a mean average precision (mAP) value of 92%, an increase of 5.2%, and the model volume is also reduced in different degrees compared with other mainstream algorithms, achieving a good balance between detection accuracy and model volume. The results show that the model has a more accurate detection result for the bird droppings-related defect detection of photovoltaic modules and can provide a reference for the detection of photovoltaic modules in real life.

关键词： photovoltaic defects fault detection yolo algorithm deep learning

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Enhanced Detection and Recognition of Road Objects in Infrared Imaging Using Multi-Scale Self-Attention

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SENSORS 2024年第16期24卷 5404页

作者： Liu, Poyi Zhang, Yunkang Guo, Guanlun Ding, Jiale Wuhan Univ Technol Sch Commun Engn Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Automot Engn Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Safety & Emergency Management Wuhan 430070 Peoples R China

In infrared detection scenarios, detecting and recognizing low-contrast and small-sized targets has always been a challenge in the field of computer vision, particularly in complex road traffic environments. Traditional target detection methods usually perform poorly when processing infrared small targets, mainly due to their inability to effectively extract key features and the significant feature loss that occurs during feature transmission. To address these issues, this paper proposes a fast detection and recognition model based on a multi-scale self-attention mechanism, specifically for small road targets in infrared detection scenarios. We first introduce and improve the DyHead structure based on the yolov8 algorithm, which employs a multi-head self-attention mechanism to capture target features at various scales and enhance the model's perception of small targets. Additionally, to prevent information loss during the feature transmission process via the FPN structure in traditional yolo algorithms, this paper introduces and enhances the Gather-and-Distribute Mechanism. By computing dependencies between features using self-attention, it reallocates attention weights in the feature maps to highlight important features and suppress irrelevant information. These improvements significantly enhance the model's capability to detect small targets. Moreover, to further increase detection speed, we pruned the network architecture to reduce computational complexity and parameter count, making the model suitable for real-time processing scenarios. Experiments on our self built infrared road traffic dataset (mainly including two types of targets: vehicles and people) show that compared with the baseline, our method achieves a 3.1% improvement in AP and a 2.5% increase in mAP on the VisDrone2019 dataset, showing significant enhancements in both detection accuracy and processing speed for small targets, with improved robustness and adaptability.

关键词： infrared detection self-attention mechanism autonomous driving yolo algorithm real-time processing

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Research on the Real-Time Detection of Red Fruit Based on the You Only Look Once algorithm

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PROCESSES 2024年第1期12卷 15页

作者： Mei, Song Ding, Wenqin Wang, Jinpeng Minist Agr & Rural Affairs Nanjing Inst Agr Mechanizat Nanjing 210014 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Coinnovat Ctr Efficient Proc & Utilizat Forest Res Nanjing Forestry Univ Nanjing 210037 Peoples R China

The real-time and accurate recognition of fruits and vegetables is crucial for the intelligent control of fruit and vegetable robots. In this research, apple picking is selected. This study proposed a lightweight, coupon-product, neural-net terminal yolo algorithm for apple image recognition. Compared with the yolo series algorithm, the tiny algorithm shows a strong relationship with the calculation speed. In traditional red fruit detection, the recognition time is generally several seconds, which is unacceptable in the real-time system. In this research, a total of 2000 apple images from different environments are used as a dataset for training and testing. The yolov4-tiny model is detailed, instructed, and used for the identification. The indicators, such as F1Score (0.92) and mAP (95.5% average), are analyzed by calculating the loss rate, accuracy rate (96.21%), and recall rate (95.47%). Finally, the algorithm shows good accuracy and high speed (no more than 5 ms) in online real-time detection.

关键词： deep learning yolo algorithm dataset real-time detection

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Annotated Lung CT Image Database 64

Annotated Lung CT Image Database

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ELMAR 64th International Symposium

作者： Ivusic, David Petrak, Antun Bozek, Jelena Grgic, Sonja Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

ISBN: (数字)9781665470032

ISBN: (纸本)9781665470032

Computed tomography (CT) of lungs provides a diagnostic tool for identifying a range of lesions and diseases visible in the obtained scans. For helping radiologists in a timely and efficient assessment of a large number of scans different machine learning methods have been applied for the detection and classification of abnormalities. However, before clinical usage of such algorithms it is necessary to achieve high accuracy of the algorithm. This is achieved through training and testing phases for which it is essential to have a database that would include a range of abnormalities in the lungs. Here we propose a novel database ALCTID (Annotated Lung CT Image Database) with regions of interest (ROI) annotated by an experienced thoracic radiologist. Database includes 170 lung CT images with a total of 307 annotated ROIs comprising a range of abnormalities, from cancerous lesions, enlarged lymph nodes to enlarged heart and edema. To demonstrate the applicability of the novel database we trained and tested convolution neural network based on the yolo (You Only Look Once) algorithm on 170 images with annotated ROIs and 170 images of healthy lungs. Out of 80 annotated ROIs in the test set, the network correctly detected 56 ROIs, with 12 false positives and 25 false negatives. The new database ALCTID is publicly available at http://***/alctid.

关键词： Lung CT Database Region of Interest Neural Network yolo algorithm ALCTID

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Detection, Recognition, and Grasping of Objects through Artificial Intelligence Using a Robotic Hand 21

Detection, Recognition, and Grasping of Objects through Arti...

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21st IFAC Conference on Technology, Culture and International Stability TECIS 2022

作者： Sejdiu, V Pajaziti, A. Rexha, G. Bajrami, X. Rrustemi, E. Kola, J. Univ Prishtina Fac Mech Engn Dept Mechatron Prishtina Kosovo Albanian Univ Dept Engn Fac Appl & Econ Sci Tirana Albania

In this paper, a robotic hand can perceive surrounding objects and grasp the object of interest. This is achieved by the implementation of the yolo algorithm for the detection of everyday objects, filtering information on the presence of man-made objects, and sending that real-time information by serial communication on the robotic hand microcontroller. Upon receiving this information, commands are sent to the robotic hand motors and the latter is compressed and takes the form of the object detected by the algorithm. For objects such as bottles and cups, the adequate shape is considered the tightening of 4 fingers except for the thumb, while for thin objects such as forks, spoons, etc. the thumb and index finger are activated. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Detection Recognition Grasping Robotic Hand Image Processing yolo algorithm

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Intellegent Coal Gangue Sorting System Based on yolov5

Intellegent Coal Gangue Sorting System Based on YOLOv5

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Asia Conference on algorithms, Computing and Machine Learning (CACML))

作者： Xin, Pan Dong, Zhang Xiao North China Elect Power Univ Beijing Peoples R China

ISBN: (数字)9781665482905

ISBN: (纸本)9781665482905

Aiming at the problem of relying on manpower and consuming a lot of resources in the sorting of gangue in the traditional coal industry, the self-made training set of gangue and coal, combined with the yolo algorithm of target detection, realized the identification and distinction of gangue and coal. Experiments show that the method is fast and can more accurately distinguish coal and gangue, and achieves the expected effect.

关键词： Coal Gangue Sorting yolo algorithm Deep Learning Target Detection

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Performance evaluation and model quantization of object detection algorithm for infrared image 7

Performance evaluation and model quantization of object dete...

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7th Asia Pacific Conference on Optics Manufacture (APCOM)

作者： Cui, Pengfei Zhang, Jinsuo Han, Bing Wu, Yi Tianjin Jinhang Inst Tech Phys Tianjin 300308 Peoples R China Northwestern Polytech Univ Xian 710072 Shaanxi Peoples R China

ISBN: (数字)9781510652095

ISBN: (纸本)9781510652095;9781510652088

Object detection technology has always been one of the important research directions in the field of computer vision. It has important application value and prospects in both civil fields and military fields. With the emergence of artificial intelligence technology, deep learning has gradually replaced the traditional algorithm with its higher accuracy. Considering that most of the current algorithms are for color image, compared with color image, infrared image contains less feature information, and it is more difficult in object detection. In this paper, different algorithms are used for object detection in infrared images, and the detection results are compared. This paper chooses yolo V5 and combines it with MobileNet to lighten the model. After lightening, the parameters are reduced by 30%, but the accuracy is only reduced by 5%. Finally, this paper quantify yolo V5 based on the model quantization method of PyTorch. After quantization, the accuracy of the model decreases by 2%.

关键词： Infrared object detection yolo algorithm Lightweight design Model quantization

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A Survey on Wild Creatures Alert System to Protect Agriculture Lands Domestic Creatures and People 1

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2nd International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Makanyadevi, K. Aarthi, M. Kavyadharsini, P. Keerthika, S. Sabitha, M. M Kumarasamy Coll Engn Dept Comp Sci & Engn Karur 639113 Tamil Nadu India

ISBN: (数字)9789811925412

ISBN: (纸本)9789811925412;9789811925405

Wild animal ready framework in ML is utilized to secure the property, fringe, and runway from wild creatures with enormous information study of a hazardous wild monster. Checking the exhibition of wild animals is utilized to secure the farmlands. In old ways of spotting creatures in paddy fields and residences embody crafted by regular eyes to notice the creature movement. It is not feasible for men to watch creature developments ceaselessly for the duration of the day. From where a desire of specific discovery of animals which penetrate appreciably in the rice fields and the agricultural lands of the individuals. The ways utilized for the prevalence of the creatures encapsulate division and item discovery strategies. This result requires difficulties in the development of returns and causes the ranch failure. This tosses back to ranchers destroying the land on account of incessant monstrosities by wild animals.

关键词： Bounding box Dataset Deep learning Machine learning Object detection yolo algorithm

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