Investigation of You Only Look Once Networks for Vision-based Small Object Detection

Yang, Li

doi:10.14569/ijacsa.2023.0140410

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…In a parallel investigation, Yang [18] conducted a comprehensive analysis comparing the performance of YOLOv5, YOLOv6, and YOLOv7 models. Interestingly, Yang's findings align closely with our own research, as he observed that the YOLOv6 model exhibited superior performance compared to its counterparts.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Detection of Small Objects in Automotive Thermal Images with Modern Deep Neural Architectures

Balon,

Knapik,

Cyganek

2023

Annals of Computer Science and Information Systems

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Thermal imaging has shown great potential for improving object detection in automotive settings, particularly in low light or adverse weather conditions. To help and further develop this industry, we extend our previously shared Thermal Automotive Dataset by more than 2000 new images and 2 novel object detecting models based on YOLOv5 and YOLOv7 architecture. We point how important is the size of the dataset. Additionally, we compare the performance of both models, to see which is more reliable and superior in terms of detecting small objects in thermal spectrum. Furthermore, we analysed how preprocessing affects thermal imaging dataset and models basing on it. The new dataset is available free from the Internet.

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Section: Discussionmentioning

confidence: 99%

Real-Time Detection of Small Objects in Automotive Thermal Images with Modern Deep Neural Architectures

Balon,

Knapik,

Cyganek

2023

Annals of Computer Science and Information Systems

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“…YOLOv3 has also been used for other detections, for example, to detect pedestrians [11], vehicles [12], and road objects [13]. Another investigation compared YOLOv5, YOLOv6, and YOLOv7 for detecting small objects [14], while YOLOv7 was used for real-time weed detection [15]. Yung et al compared YOLOv5, YOLOv6, and YOLOv7 to detect safety helmets workers wear [16].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Driver and Passenger Without Seat Belt using YOLOv8

Sugiharto,

Kusumaningrum

2023

IJACSA

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The issue of traffic accident fatalities is a serious concern on a global scale, and one of the contributing factors is the failure of drivers to adhere to seat belt usage. A notable challenge arises from the limited availability of law enforcement personnel monitoring this particular issue. In this context, there is a compelling need to implement an automated detection system. The development of this system using YOLOv5 has been done. However, there are weaknesses related to the length of training and detection time. Therefore, this paper proposed a new system using the YOLOv8 method to detect drivers and passengers who violate seat belt regulations. The proposed system is divided into three subsystems: windshield detection, passenger classification, and seat belt classification. YOLOv8 is the latest version of the YOLO (You Only Look Once) method and has been proven to provide better performance than previous versions. Furthermore, this paper also compared five YOLOv8 models, namely YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. The proposed model is trained and tested using image data collected from several roads in Indonesia. The experiment results show that the YOLOv8s model produced the best mean Average Precision (mAP) of 0.960 for windshield detection. YOLOv8s-cls and YOLOv8l-cls models achieved the same accuracy of 0.8923 for passenger classification. The YOLOv8l-cls model produced the best accuracy of 0.8846 for seat belt classification. In addition, the proposed method can increase mAP and training time for windshield detection compared to YOLOv5.

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Garbage Detection using YOLO Algorithm for Urban Management in Bangkok

Panmuang,

Rodmorn

2024

Wseas Transactions on Computer Research

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Garbage problems in urban areas are becoming more serious as the population increases, resulting in community garbage, including Bangkok, the capital of Thailand, being affected by pollution from rotten waste. Therefore, this research aims to apply deep learning technology to detect images from CCTV cameras in urban areas of Bangkok by using YOLO to detect images from CCTV cameras in urban areas of Bangkok, using YOLO to detect 1,383 images of overflowing garbage bins, classified into 2 classes: garbage class and bin class. YOLO in each version was compared, consisting of YOLOv5n, YOLOv6n, YOLOv7, and YOLOv8n. The comparison results showed that YOLOv5n was able to classify classes with an accuracy of 94.50%, followed by YOLOv8n at 93.80%, YOLOv6n at 71.60%, and YOLOv7 at 24.60%, respectively. The results from this research can be applied to develop a mobile or web application to notify of overflowing garbage bins by integrating with CCTV cameras installed in communities to monitor garbage that is overflowing or outside the bin and notify relevant agencies or the locals. This will allow for faster and more efficient waste management.

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Investigation of You Only Look Once Networks for Vision-based Small Object Detection

Cited by 3 publications

References 35 publications

Real-Time Detection of Small Objects in Automotive Thermal Images with Modern Deep Neural Architectures

Real-Time Detection of Small Objects in Automotive Thermal Images with Modern Deep Neural Architectures

Automated Detection of Driver and Passenger Without Seat Belt using YOLOv8

Garbage Detection using YOLO Algorithm for Urban Management in Bangkok

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