TraCon: A novel dataset for real-time traffic cones detection using deep learning

Katsamenis, Iason; Karolou, Eleni Eirini; Davradou, Agapi; Protopapadakis, Eftychios; Doulamis, Anastasios; Doulamis, Nikolaos; Kalogeras, Dimitris

doi:10.48550/arxiv.2205.11830

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…By detecting the color of the hardhat, the project manager can easily check whether the workers are working within their scope of responsibility. The YOLO model was selected to train the hardhat color detection model due to its extraordinary performances in variable construction object detection tasks [1,6,12,24]. YOLO-v5 holds the best performance among current YOLO models [12,25,26].…”

Section: Hardhat Detectionmentioning

confidence: 99%

Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection

Chen

Lian

et al. 2022

Buildings

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The Internet of Things (IoT), including sensors, computer vision (CV), robotics, and visual reality technologies, is widely used in the construction industry to facilitate construction management in productivity and safety control. The application of such technologies in real construction projects requires high-quality computing resources, the network for data transferring, a near real-time response, geographical closeness to the smart environments, etc. Most existing research has focused on the first step of method development and has neglected the further deployment step. For example, when using CV-based methods for construction site monitoring, internet-connected cameras must transmit large quantities of high-quality data to the central office, which may be located thousands of miles away. Not only the quality may suffer due to latency, but the wideband cost can be astronomical. Edge computing devices and systems help solve this problem by providing a local source to process the data. The goal of this study is to embed the CV-based method into devices and thus to develop a practical edge computing system for vision-based construction resource detection, which can provide automatic construction with high-quality and more applicable service. Specifically, this study first developed a CV-based hardhat color detection model to manage workers in different tasks. Then, the model was embedded into a Raspberry Pi microcomputer mainboard for video data processing, and the performance was compared with the local computer to validate the feasibility of the proposed method.

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