Research Area:  Machine Learning

Abstract:

Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of deep learning-based object detection in challenging environments. However, there is no consolidated reference to cover the state of the art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present a quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

Keywords:  

Author(s) Name:  Muhammad Ahmed ,Khurram Azeem Hashmi ,Alain Pagani ,Marcus Liwicki ,Didier Stricker and Muhammad Zeshan Afzal

Publisher name:  MDPI

DOI:  10.3390/s21155116

Volume Information:  Volume 21 Issue 15

Paper Link:   https://www.mdpi.com/1424-8220/21/15/5116