Abstract
As the development of object detection technology in computer vision, identifying objects is always an active yet challenging task, and even more efficient and accurate requirements are being imposed on state-of-the-art algorithms. However, many algorithms perform object box regression based on RPN(Region Proposal Network) and anchors, which cannot accurately describe the shape information of the object. In this paper, we propose a new object detection method called Field Network (FN) and Region Fitting Algorithm (RFA). It can solve these problems by Center Field. Center field reflects the probability of the pixel approaching the object center. Different from the previous methods, we abandoned anchors and ROI technologies, and propose the concept of Field. Field is the intensity of the object area, reflecting the probability of the object in the area. Based on the distribution of the probability density of the object center in the visual field perception area, we add the Object Field in the output part. And we abstract it into an Elliptic Field with normal distribution and use RFA to fit objects. Additionally, we add two fields to predict the x,y components of the object direction which contain the neural units in the field array. We extract the objects through these Fields. Moreover, our model is relatively simple and have smaller size, which is only 73 M. Our method improves performance considerably over baseline systems on DOTA, MS COCO and PASCAL VOC datasets, with overall performance competitive with recent state-of-the-art systems.
Highlights
Owing to the continual development of computer vision technology in recent years, object detection has entered a new era [1,2,3]
We proposed an algorithm based on a field—called Field Network (FN)—for object detection, which can effectively balance speed and accuracy
Our algorithm can detect the objects, and determine the direction. Even if it is a big image, we can detect it by spray painting without cutting
Summary
Owing to the continual development of computer vision technology in recent years, object detection has entered a new era [1,2,3]. Semantic segmentation has strong learning ability for pixel-by-pixel classification and does not require very large models to support coordinate regression of high-precision object positions [5,6,22,23,24]. To solve the aforementioned problems, in this paper we propose a new object detection model called Field Network (FN). Based on the Field, our framework can distinguish the overlapping regions of the same object on the basis of Center Field From this we can get the center coordinates, the range of the area, and the total number of objects for each one. We design a Field-based object Region Fitting Algorithm (RFA), which abandons some traditional techniques and makes the algorithm efficient and accurate for object detection. We can get the direction of the object through the Direction Field by regressing the direction vector
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