Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

doi:10.35940/ijrte.b1154.0782s319

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…As a result, Faster RCNN takes longer to compute than YOLOv3 and YOLOv4 (Alganci et al, 2020). The accuracy results in Faster RCNN are higher than YOLO (Dixit et al, 2019). The research of Dixit et al (2019) shows that the precision of Faster RCNN is the highest, but the speed of object detection is the slowest compared to other object detection models.…”

Section: Faster Rcnn Resultsmentioning

confidence: 98%

Comparison Using Intelligent Systems for Data Prediction and Near Miss Detection Techniques

Ming Lim,

Sathasivam,

Ismail

et al. 2023

JST

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Malaysia ranks third among ASEAN countries in terms of deaths due to accidents, with an alarming increase in the number of fatalities each year. Road conditions contribute significantly to near-miss incidents, while the inefficiency of installed CCTVs and the lack of monitoring system algorithms worsen the situation. The objective of this research is to address the issue of increasing accidents and fatalities on Malaysian roads. Specifically, the study aims to investigate the use of video technology and machine learning algorithms for the car detection and analysis of near-miss accidents. To achieve this goal, the researchers focused on Penang, where the MBPP has deployed 1841 CCTV cameras to monitor traffic and document near-miss accidents. The study utilised the YOLOv3, YOLOv4, and Faster RCNN algorithms for vehicle detection. Additionally, the study employed image processing techniques such as Bird’s Eye View and Social Distancing Monitoring to detect and analyse how near misses occur. Various video lengths (20s, 40s, 60s and 80s) were tested to compare the algorithms’ error detection percentage and test duration. The results indicate that Faster RCNN beats YOLOv3 and YOLOV4 in car detection with low error detection, whereas YOLOv3 and YOLOv4 outperform near-miss detection, while Faster RCNN does not perform it. Overall, this study demonstrates the potential of video technology and machine learning algorithms in near-miss accident detection and analysis. Transportation authorities can better understand the causes of accidents and take appropriate measures to improve road safety using these models. This research can be a foundation for further traffic safety and accident prevention studies.

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Section: Faster Rcnn Resultsmentioning

confidence: 98%

Comparison Using Intelligent Systems for Data Prediction and Near Miss Detection Techniques

Ming Lim,

Sathasivam,

Ismail

et al. 2023

JST

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“…To address this limitation, the system could enhance its capabilities by integrating demographic feature extraction through voice analysis. Furthermore, the computational efficiency and complexity of the face attribute extraction module could be enhanced by leveraging the advantages of the You Only Look Once (YOLO) algorithm due to its superior efficiency and suitability for real-time applications [50].…”

Section: Movies Recommendationmentioning

confidence: 99%

A Hybrid Movies Recommendation System Based on Demographics and Facial Expression Analysis using Machine Learning

Balfaqih

2023

IJACSA

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Cinemas and digital platforms offer an extensive array of content requiring tailored filtering to cater to individual preferences. While recommender systems prove invaluable for this purpose, conventional movie recommendations tend to emphasize specific attributes, leading to a reduction in overall accuracy and reliability. Notably, the extraction process of facial temporal attributes exhibits a suboptimal level of accuracy, thereby influencing the classification of attributes and the overall accuracy of the recommendation system. This article introduces a hybrid recommender system that seamlessly integrates collaborative filtering and content-based methodologies. The system takes into account crucial factors such as age, gender, emotion, and genre attributes. Films undergo an initial categorization based on genre, with a subsequent selection of the most representative genres to ascertain group preferences. Ratings for these selected movies are then predicted and organized in descending order. Employing Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models, the system achieves real-time extraction of facial attributes, particularly enhancing the accuracy of emotion attribute extraction through sequential processing. The CNN model demonstrates a commendable 55.3% accuracy score, the LSTM model excels with a 59.1% score, while the combined CNN and LSTM models showcase an impressive 60.2% accuracy.The performance of the recommendation system is rigorously evaluated using standard metrics, including precision, recall, and F1-measure. Results underscore the superior performance of the proposed system across various testing scenarios compared to the established benchmark. Nevertheless, it is noteworthy that the precision of the benchmark marginally surpasses the proposed system in the age groups of 8-14 and 15-24.

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“…An important feature of this architecture is that convolution layers are applied to the image once, unlike such architectures as R-CNN [23][24][25] and Faster R-CNN [8], which provides a multiple increase in the image processing speed without significant losses in accuracy: one image is processed 1000 times faster using YOLO than R-CNN, and 100 times faster than Fast R-CNN [24].…”

Section: Object Detectionmentioning

confidence: 99%

Real-time monitoring of traffic parameters

et al. 2020

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This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.

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Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

Cited by 16 publications

References 14 publications

Comparison Using Intelligent Systems for Data Prediction and Near Miss Detection Techniques

Comparison Using Intelligent Systems for Data Prediction and Near Miss Detection Techniques

A Hybrid Movies Recommendation System Based on Demographics and Facial Expression Analysis using Machine Learning

Real-time monitoring of traffic parameters

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