Review of Deep Learning Applied to Occluded Object Detection

Abstract

Occluded object detection has long been a difficulty and hot topic in the field of computer vision. Based on convolutional neural network, the deep learning takes the object detection task as a classification and regression task to handle, and obtains remarkable achievements. The mask confuses the features of object when the object is occluded, making the deep convolutional neural network cannot handle it well and reducing the performance of detector in ideal scenes. Considering the universality of occlusion in reality, the effective detection of occluded object has important research value. In order to further promote the development of occluded object detection, this paper makes a comprehensive summary of occluded object detection algorithms, and makes a reasonable classification and analysis. First of all, based on a simple overview of object detection, this paper introduces the relevant theoretic background, research difficulties and datasets about occluded object detection. After, this paper focuses on the algo-rithms to improve the performance of occluded object detection from the aspects of object structure, loss function, non-maximum suppression and semantic partial. This paper compares the performance of different detection algo-rithms after summarizing the relationship and development of various algorithms. Finally, this paper points out the difficulties of occluded object detection and looks forward to its future development directions.

Keywords

• |occluded object detection|deep learning|loss function|non-maximum suppression|semantic partial