YOLIC: An Efficient Method for Object Localization and Classification on Edge Devices

• URL: http://arxiv.org/abs/2307.06689v3
• Date: Sun, 30 Jul 2023 09:29:43 GMT
• Title: YOLIC: An Efficient Method for Object Localization and Classification on Edge Devices
• Authors: Kai Su, Yoichi Tomioka, Qiangfu Zhao, Yong Liu
• Abstract summary: You Only Look at Interested Cells" (YOLIC) is an efficient method for object localization and classification on edge devices. By adopting Cells of Interest for classification instead of individual pixels, YOLIC encapsulates relevant information, reduces computational load, and enables rough object shape inference. This paper presents extensive experiments on multiple datasets to demonstrate that YOLIC achieves detection performance comparable to the state-of-the-art YOLO algorithms.
• Score: 10.058627390826967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of Tiny AI, we introduce ``You Only Look at Interested Cells" (YOLIC), an efficient method for object localization and classification on edge devices. Through seamlessly blending the strengths of semantic segmentation and object detection, YOLIC offers superior computational efficiency and precision. By adopting Cells of Interest for classification instead of individual pixels, YOLIC encapsulates relevant information, reduces computational load, and enables rough object shape inference. Importantly, the need for bounding box regression is obviated, as YOLIC capitalizes on the predetermined cell configuration that provides information about potential object location, size, and shape. To tackle the issue of single-label classification limitations, a multi-label classification approach is applied to each cell for effectively recognizing overlapping or closely situated objects. This paper presents extensive experiments on multiple datasets to demonstrate that YOLIC achieves detection performance comparable to the state-of-the-art YOLO algorithms while surpassing in speed, exceeding 30fps on a Raspberry Pi 4B CPU. All resources related to this study, including datasets, cell designer, image annotation tool, and source code, have been made publicly available on our project website at https://kai3316.github.io/yolic.github.io

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