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：在微型人工智能领域，我们提出"你只关注感兴趣细胞"（YOLIC）——一种面向边缘设备的高效对象定位与分类方法。该方法无缝融合语义分割与目标检测的优势，兼具卓越的计算效率与精度。通过采用感兴趣细胞进行分类而非单个像素，YOLIC能够封装相关信息、降低计算负载，并实现对象形状的粗略推断。尤为重要的是，由于YOLIC利用预设细胞结构提供潜在对象位置、尺寸与形状信息，从而避免了边界框回归的需求。针对单标签分类的局限性，该方法对每个细胞应用多标签分类策略，有效识别重叠或邻近对象。本文在多个数据集上开展广泛实验，表明YOLIC在检测性能上媲美最先进的YOLO算法，同时速度更优——在树莓派4B CPU上可实现超过30fps。本研究涉及的所有资源，包括数据集、细胞设计器、图像标注工具及源代码，均已通过项目网站https://kai3316.github.io/yolic.github.io 公开提供。