YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations
Ensuring operational safety within high-risk environments, such as construction sites, is paramount, especially for tower crane operations where distractions can lead to severe accidents. Despite existing behavioral monitoring approaches, the task of identifying small yet hazardous objects like mobi...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1999-4893/18/1/27 |
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| author | Dong Ding Zhengrong Deng Rui Yang |
| author_facet | Dong Ding Zhengrong Deng Rui Yang |
| author_sort | Dong Ding |
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| description | Ensuring operational safety within high-risk environments, such as construction sites, is paramount, especially for tower crane operations where distractions can lead to severe accidents. Despite existing behavioral monitoring approaches, the task of identifying small yet hazardous objects like mobile phones and cigarettes in real time remains a significant challenge in ensuring operator compliance and site safety. Traditional object detection models often fall short in crane operator cabins due to complex lighting conditions, cluttered backgrounds, and the small physical scale of target objects. To address these challenges, we introduce YOLO-TC, a refined object detection model tailored specifically for tower crane monitoring applications. Built upon the robust YOLOv7 architecture, our model integrates a novel channel–spatial attention mechanism, ECA-CBAM, into the backbone network, enhancing feature extraction without an increase in parameter count. Additionally, we propose the HA-PANet architecture to achieve progressive feature fusion, addressing scale disparities and prioritizing small object detection while reducing noise from unrelated objects. To improve bounding box regression, the MPDIoU Loss function is employed, resulting in superior accuracy for small, critical objects in dense environments. The experimental results on both the PASCAL VOC benchmark and a custom dataset demonstrate that YOLO-TC outperforms baseline models, showcasing its robustness in identifying high-risk objects under challenging conditions. This model holds significant promise for enhancing automated safety monitoring, potentially reducing occupational hazards by providing a proactive, resilient solution for real-time risk detection in tower crane operations. |
| format | Article |
| id | doaj-art-5c623d7826d948b78e8721940d62fcfd |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-5c623d7826d948b78e8721940d62fcfd2025-01-24T13:17:31ZengMDPI AGAlgorithms1999-48932025-01-011812710.3390/a18010027YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane OperationsDong Ding0Zhengrong Deng1Rui Yang2Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, ChinaGuangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, ChinaGuangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, ChinaEnsuring operational safety within high-risk environments, such as construction sites, is paramount, especially for tower crane operations where distractions can lead to severe accidents. Despite existing behavioral monitoring approaches, the task of identifying small yet hazardous objects like mobile phones and cigarettes in real time remains a significant challenge in ensuring operator compliance and site safety. Traditional object detection models often fall short in crane operator cabins due to complex lighting conditions, cluttered backgrounds, and the small physical scale of target objects. To address these challenges, we introduce YOLO-TC, a refined object detection model tailored specifically for tower crane monitoring applications. Built upon the robust YOLOv7 architecture, our model integrates a novel channel–spatial attention mechanism, ECA-CBAM, into the backbone network, enhancing feature extraction without an increase in parameter count. Additionally, we propose the HA-PANet architecture to achieve progressive feature fusion, addressing scale disparities and prioritizing small object detection while reducing noise from unrelated objects. To improve bounding box regression, the MPDIoU Loss function is employed, resulting in superior accuracy for small, critical objects in dense environments. The experimental results on both the PASCAL VOC benchmark and a custom dataset demonstrate that YOLO-TC outperforms baseline models, showcasing its robustness in identifying high-risk objects under challenging conditions. This model holds significant promise for enhancing automated safety monitoring, potentially reducing occupational hazards by providing a proactive, resilient solution for real-time risk detection in tower crane operations.https://www.mdpi.com/1999-4893/18/1/27computer visionsmall object detectionYOLOv7attention mechanismfeature fusion |
| spellingShingle | Dong Ding Zhengrong Deng Rui Yang YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations Algorithms computer vision small object detection YOLOv7 attention mechanism feature fusion |
| title | YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations |
| title_full | YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations |
| title_fullStr | YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations |
| title_full_unstemmed | YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations |
| title_short | YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations |
| title_sort | yolo tc an optimized detection model for monitoring safety critical small objects in tower crane operations |
| topic | computer vision small object detection YOLOv7 attention mechanism feature fusion |
| url | https://www.mdpi.com/1999-4893/18/1/27 |
| work_keys_str_mv | AT dongding yolotcanoptimizeddetectionmodelformonitoringsafetycriticalsmallobjectsintowercraneoperations AT zhengrongdeng yolotcanoptimizeddetectionmodelformonitoringsafetycriticalsmallobjectsintowercraneoperations AT ruiyang yolotcanoptimizeddetectionmodelformonitoringsafetycriticalsmallobjectsintowercraneoperations |