YOLO-Based Object Detection for Separate Collection of Recyclables and Capacity Monitoring of Trash Bins

Wahyutama, Aria Bisma; Hwang, Mintae

doi:10.3390/electronics11091323

Cited by 32 publications

(12 citation statements)

References 9 publications

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“…These results were in line with previous studies [ 51 , 52 , 53 , 54 ]. Moreover, YOLO models have advantages in terms of detection speed and low hardware requirements [ 55 , 56 , 57 , 58 , 59 , 60 ], which could be used for future real-time monitoring or deployment in lower hardware devices. For motion capture, this study utilized OpenPose technology (COCO model) to obtain time series motion data, which was used for motion identification.…”

Section: Discussionmentioning

confidence: 99%

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

Xue

et al. 2023

Sensors

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The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers’ unsafe behaviors is the computer vision-based intelligent monitoring system. However, most of the existing research or products focused only on the workers’ behaviors (i.e., motions) recognition, limited studies considered the interaction between man-machine, man-material or man-environments. Those interactions are very important for judging whether the workers’ behaviors are safe or not, from the standpoint of safety management. This study aims to develop a new method of identifying construction workers’ unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN (Spatial Temporal Graph Convolutional Networks) and YOLO (You Only Look Once), which could provide more direct and valuable information for safety management. In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with workers. Then, an ST-GCN model was trained to detect and identify workers’ behaviors. Lastly, a decision algorithm was developed considering interactions between man-machine/material, based on YOLO and ST-GCN results. Results show good performance of the developed method, compared to only using ST-GCN, the accuracy was significantly improved from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, respectively, in the identification of the following three kinds of behaviors, throwing (throwing hammer, throwing bottle), operating (turning on switch, putting bottle), and crossing (crossing railing and crossing obstacle). The findings of the study have some practical implications for safety management, especially workers’ behavior monitoring and management.

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Section: Discussionmentioning

confidence: 99%

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

Xue

et al. 2023

Sensors

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“…Finally, the model is subjected to testing, and the accuracy of the testing data, as prepared earlier, is assessed. [33]. The source of this data is a crucial determinant of the model's performance and reliability.…”

Section: Methodsmentioning

confidence: 99%

“…Sequential Progression of Research Stages in Motorcycle Rider Detection 2.1. Data Collection Data collection serves as the foundational step in this research, providing the raw materials necessary for the development and training of the YOLO model[33]. The source of this data is a crucial determinant of the model's performance and reliability.…”

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confidence: 99%

Transforming traffic surveillance: a YOLO-based approach to detecting helmetless riders through CCTV

Ariwibowo,

Ismi

2022

SITech

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CCTV systems, while ubiquitous for traffic surveillance in Indonesian roadways, remain underutilized in their potential. The integration of AI and Computer Vision technologies can transform CCTV into a valuable tool for law enforcement, specifically in monitoring and addressing helmet non-compliance among motorcycle riders. This study aims to develop an intelligent system for the accurate detection of helmetless motorcyclists using image analysis. The approach relies on deep learning, involving the creation of a dataset with 764 training images and 102 testing images. A deep convolutional neural network with 23 layers is configured, trained with a batch size of 10 over ten epochs, and employs the YOLO method to identify objects in images and subsequently detect helmetless riders. Accuracy assessment is carried out using the mean Average Precision (mAP) method, resulting in a notable 82.81% detection accuracy for riders without helmets and 75.78% for helmeted riders. The overall mAP score is 79.29%, emphasizing the system's potential to substantially improve road safety and law enforcement efforts

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“…To overcome the limitations of this algorithm, YOLO v4 was proposed in 2020 to improve the performance of YOLO v3 [18][19][20]. YOLO v4 improves the feature representation and detection of the objects using advanced techniques like a pseudo-attention network (PAN), path aggregation network (PANet), and spatial attention module (SAM), which improve both accuracy and the speed of detection ability [18,21]. However, the limitation when working with small objects has not been overcome.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

Kumar,

Shih,

Guo

et al. 2024

Future Internet

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Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.

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YOLO-Based Object Detection for Separate Collection of Recyclables and Capacity Monitoring of Trash Bins

Cited by 32 publications

References 9 publications

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

Transforming traffic surveillance: a YOLO-based approach to detecting helmetless riders through CCTV

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

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