You Only Look at Interested Cells: Real-Time Object Detection Based on Cell-Wise Segmentation

Abstract

In this paper, we study real-time object detection based on cell-wise segmentation. Existing object detection methods usually focus on detecting interesting object's positions and sizes and demand expensive computing resources. This process makes it difficult to achieve high-speed and high-precision detection with low-cost devices. We propose a method called You Only Look at Interested Cells or in-short YOLIC to solve the problem by focusing on predefined interested cells (i.e., subregions) in an image. A key challenge here is how to predict the object types contained in all interested cells efficiently, all at once. Instead of using multiple predictors for all interested cells, we use only one deep learner to classify all interested cells. In other words, YOLIC applies the concept of multi-label classification for object detection. YOLIC can use exiting classification models without any structural change. The main point is to define a proper loss function for training. Using on-road risk detection as a test case, we confirmed that YOLIC is significantly faster and accurate than YOLO-v3 in terms of FPS and F1-score.