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. 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, effectively recognizing overlapping or closely situated objects. This paper presents extensive experiments on multiple datasets, demonstrating 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

翻译：在Tiny AI领域，我们提出“仅关注感兴趣单元”（YOLIC）——一种面向边缘设备的高效目标定位与分类方法。该方法无缝融合语义分割与目标检测的优势，在实现卓越计算效率的同时保持高精度。通过采用感兴趣单元分类替代逐像素分类，YOLIC既能封装相关信息，又可降低计算负载，并支持粗略目标形状推断。尤为重要的是，该方法无需边界框回归，其充分利用预定义的单元配置直接提供潜在目标的位置、尺寸与形状信息。针对单标签分类的局限性，我们对每个单元应用多标签分类策略，有效识别重叠或紧密邻近的目标。本文在多个数据集上开展大量实验，结果表明YOLIC在达到与最先进YOLO算法相当的检测性能的同时，在速度上更具优势——在树莓派4B CPU上可超过30fps。本研究的全部资源（包括数据集、单元设计器、图像标注工具及源代码）均已公开于项目网站：https://kai3316.github.io/yolic.github.io