{
  "id": "2303.02735",
  "version": "v2",
  "published": "2023-03-05T18:02:54.000Z",
  "updated": "2023-03-17T15:49:09.000Z",
  "title": "Scalable Object Detection on Embedded Devices Using Weight Pruning and Singular Value Decomposition",
  "authors": [
    "Dohyun Ham",
    "Jaeyeop Jeong",
    "June-Kyoo Park",
    "Raehyeon Jeong",
    "Seungmin Jeon",
    "Hyeongjun Jeon",
    "Yewon Lim"
  ],
  "comment": "8 pages, 3 figures. A report of the project done as part of the Yonsei-Roboin project for the 2nd semester, 2022",
  "categories": [
    "cs.CV",
    "cs.AI"
  ],
  "abstract": "This paper presents a method for optimizing object detection models by combining weight pruning and singular value decomposition (SVD). The proposed method was evaluated on a custom dataset of street work images obtained from https://universe.roboflow.com/roboflow-100/street-work. The dataset consists of 611 training images, 175 validation images, and 87 test images with 7 classes. We compared the performance of the optimized models with the original unoptimized model in terms of frame rate, mean average precision (mAP@50), and weight size. The results show that the weight pruning + SVD model achieved a 0.724 mAP@50 with a frame rate of 1.48 FPS and a weight size of 12.1 MB, outperforming the original model (0.717 mAP@50, 1.50 FPS, and 12.3 MB). Precision-recall curves were also plotted for all models. Our work demonstrates that the proposed method can effectively optimize object detection models while balancing accuracy, speed, and model size.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2023-03-17T15:49:09.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "singular value decomposition",
      "scalable object detection",
      "weight pruning",
      "embedded devices",
      "frame rate"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 8,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}