Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Survey and Performance Analysis of Object Detection in Challenging Environments

Version 1 : Received: 22 June 2021 / Approved: 23 June 2021 / Online: 23 June 2021 (16:01:33 CEST)

A peer-reviewed article of this Preprint also exists.

Ahmed, M.; Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, M.Z. Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments. Sensors 2021, 21, 5116. Ahmed, M.; Hashmi, K.A.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, M.Z. Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments. Sensors 2021, 21, 5116.

Journal reference: Sensors 2021, 21, 5116
DOI: 10.3390/s21155116

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 object detection in challenging environments. However, there is no consolidated reference to cover 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 the 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

Object detection; challenging environments; low-light; image enhancement; complex environments; state-of-the-art; deep neural networks; computer vision; performance analysis.

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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