BED: A Real-Time Object Detection System for Edge Devices

Guanchu, Wang,; Bhat, Zaid Pervaiz; Jiang, Zhimeng; Chen, Yiwei; Zha, Daochen; Reyes, Alfredo Costilla; Niktash, A.; Gorkem, Ulkar,; Okman, Erman; Cai, Xuanting; Hu, X. Joan

doi:10.1145/3511808.3557168

Cited by 7 publications

(1 citation statement)

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“…In these systems, object detection algorithms are used to detect human intrusions early by processing images from surveillance cameras [3]. With large systems, processing locally is essential because it is timely and reduces the load on the central server [4]. However, implementing deep learning algorithms on such device surveillance cameras is generally difficult because of hardware constraints [5].…”

Section: Introductionmentioning

confidence: 99%

Backbone search for object detection for applications in intrusion warning systems

Thuan,

Huong,

Hong

2024

IJ-AI

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<span>In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset. </span>

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Section: Introductionmentioning

confidence: 99%