DETECTION AND RECOGNITION OF ROAD SIGNS USING YOLOv5

In the field of deep learning, a convolutional neural network is a class of artificial neural networks that became dominant in various computer vision tasks, which is widely used to solve complex problems in various areas, including driver assistance systems in the auto- motive field. Convolutional neural networks overcome the limitations of others conventional machine learning approaches since they are designed to automatically and adaptively learn the spatial characteristics of features in an image. In this paper, we are going to evaluate the inference and accuracy of YOLOv5s, for effective traffic sign detection in various environments. The results generated upon five classes gives satisfaction by 63.7% for the mean average precision, and over 80% in accordance to 5 categories set in this study. This article compared to YOLOV4 based CSP-DarkNet53 using Indonesia Traffic Signs generate better precision

Traffic sign detection optimization using color and shape segmentation as pre-processing system

One of performance indicator of an Autonomous Vehicle (AV) is its ability to accomodate rapid environment changing; and performance of traffic sign detection (TSD) system is one of them. A low frame rate of TSD impacts to late decision making and may cause to a fatal accident. Meanwhile, adding any GPU to TSD will significantly increases its cost and make it unaffordable. This paper proposed a pre-processing system for TSD which implement a color and a shape segmentation to increase the system speed. These segmentation systems filter input frames such that the number of frames sent to AI system is reduced. As a result, workload of AI system is decreased and its frame rate increases. HSV threshold is used in color segmentation to filter frames with no desired color. This algorithm ignores the saturation when performing color detection. Further, an edge detection feature is employed in shape segmentation to count the total contours of an object. Using German Traffic Sign Recognition Benchmark dataset as model, the pre-processing system filters 97% of frames with no traffic sign objects and has an accuracy of 88%. TSD system proposed allows a frame rate improvement up to 32 FPS when YOLO algorithm is used

Cyclist Detection, Tracking, and Trajectory Analysis in Urban Traffic Video Data

The major objective of this thesis work is examining computer vision and machine learning detection methods, tracking algorithms and trajectory analysis for cyclists in traffic video data and developing an efficient system for cyclist counting. Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, a vision-based method for gathering cyclist count data at intersections and road segments is developed. First, we develop methodology for an efficient detection and tracking of cyclists. The combination of classification features along with motion based properties are evaluated to detect cyclists in the test video data. A Convolutional Neural Network (CNN) based detector called You Only Look Once (YOLO) is implemented to increase the detection accuracy. In the next step, the detection results are fed into a tracker which is implemented based on the Kernelized Correlation Filters (KCF) which in cooperation with the bipartite graph matching algorithm allows to track multiple cyclists, concurrently. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The trajectory comparison is performed based on semantic similarity approach. The proposed counting method is the first cyclist counting method that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model

The widespread adoption of Image Processing has propelled Object Recognition (OR) models into essential roles across various applications, demonstrating the power of AI and enabling crucial services. Among the applications, traffic sign recognition stands out as a popular research topic, given its critical significance in the development of autonomous vehicles. Despite their significance, real-world challenges, such as alterations to traffic signs, can negatively impact the performance of OR models. This study investigates the influence of altered traffic signs on the accuracy and effectiveness of object recognition, employing a publicly available dataset to introduce alterations in shape, color, content, visibility, angles and background. Focusing on the YOLOv7 (You Only Look Once) model, the study demonstrates a notable decline in detection and classification accuracy when confronted with traffic signs in unusual conditions including the altered traffic signs. Notably, the alterations explored in this study are benign examples and do not involve algorithms used for generating adversarial machine learning samples. This study highlights the significance of enhancing the robustness of object detection models in real-life scenarios and the need for further investigation in this area to improve their accuracy and reliability

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

Deteksi Bola dan Gawang dengan Metode YOLO Menggunakan Kamera Omnidirectional pada Robot KRSBI-B

This research is a form of development of object detection capabilities on wheeled soccer robots using an omnidirectional camera with the You Only Look Once (YOLO) method where the results show that the robot can detect more than one object, namely the ball and the goal on the green field. This study uses the KRSBI-Wheeled UAD robot using an omnidirectional camera as a tool to carry out the detection process and then uses OpenCV 4.0, Deep Learning, and a laptop as a place to create a detection model, as well as balls and goals as objects to be detected. The results obtained from this study are that the two types of YOLO models tested, namely YOLOv3 and YOLOv3-Tiny can detect ball and goal objects in two different types of frame sizes, namely 320x320 and 416x416 which can be seen from the performance of the YOLOv3 model which has an mAP value of 76%. on the 320x320 frame and an mAP value of 87.5% in the 416x416 frame then the YOLOv3-Tiny model has an mAP value of 68.1% in the 320x320 frame and an mAP value of 75.5% in the 416x416 frame where the YOLOv3 model can detect both object class is much more stable compared to YOLOv3-Tiny. Penelitian ini merupakan bentuk pengembangan dari kemampuan deteksi objek pada robot sepak bola beroda dengan menggunakan kamera omnidirectional dengan metode You Only Look Once (YOLO) dimana hasil penelitian menunjukkan bahwa robot dapat mendeteksi lebih dari satu objek yaitu bola dan gawang di atas lapangan hijau. Penelitian ini menggunakan robot KRSBI-Beroda UAD dengan memakai kamera omnidirectional sebagai alat untuk melakukan proses pendeteksian lalu menggunakan OpenCV 4.0, Deep Learning, dan laptop sebagai tempat membuat model pendeteksian, serta bola dan gawang sebagai objek yang akan dideteksi. Hasil yang didapatkan dari penelitian ini yaitu kedua jenis model YOLO yang diuji yaitu YOLOv3 dan YOLOv3-Tiny dapat mendeteksi objek bola dan gawang pada dua jenis ukuran frame yang berbeda yaitu 320x320 dan 416x416 yang dapat dilihat dari performa pada model YOLOv3 memiliki nilai mAP sebesar 76% pada frame 320x320 dan serta nilai mAP sebesar 87,5% pada frame 416x416 lalu pada model YOLOv3-Tiny memiliki nilai mAP sebesar 68,1% pada frame 320x320 serta nilai mAP sebesar 75,5% pada frame 416x416 yang dimana model YOLOv3 dapat mendeteksi kedua kelas objek jauh lebih stabil dibandingkan dengan model YOLOv3-Tiny