TY  - CONF
Y1  - 2021///
SN  - 9781665449861
PB  - Institute of Electrical and Electronics Engineers Inc.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114457988&doi=10.1109%2fICCAR52225.2021.9463487&partnerID=40&md5=ddfb3c637b4ce3c74f8730b04b16873e
A1  - Jaffari, R.
A1  - Hashmani, M.A.
A1  - Reyes-Aldasoro, C.C.
A1  - Aziz, N.
A1  - Rizvi, S.S.H.
EP  - 302
AV  - none
N2  - Efficient detection of thin objects, from stationary or moving images, is significant in a variety of research areas. These research areas include but are not limited to electric power line detection systems, sperm tail detection for clinical sperm research, mooring lines detection, road-lane line detection for autonomous vehicles, and cracks detection for the integrity assessment of building structures. However, the detection of thin objects is a challenging computer vision task owing to the slimmer and less compact nature of these objects. Moreover, the complexity present in certain images, such as the background clutter, further adds to this problem of accurately detecting thin objects. In this work, we investigate a series of state-of-the-art deep learning detectors for thin objects' detection. The detectors examined in this work were: EfficientDet, YOLOv5 and U-Net. The experimental results of this study reveal that generic state-of-the-art deep detectors are not suitable for detecting thin objects due to their reliance on coarse bounding boxes and/or excessive pixel-level computations while the application-specific detectors possess poor generalization capabilities and do not work accurately outside their domains. These empirical findings indicate the necessity of the identification of critical factors affecting thin objects detection and the subsequent design of a generic thin objects' detector. © 2021 IEEE.
N1  - cited By 6; Conference of 7th International Conference on Control, Automation and Robotics, ICCAR 2021 ; Conference Date: 23 April 2021 Through 26 April 2021; Conference Code:171166
KW  - Agricultural robots; Clinical research; Computer vision; Deep learning; Electric lines; Mooring cables; Robotics
KW  -  Application specific; Background clutter; Building structure; Efficient detection; Electric power lines; Experimental investigations; Generalization capability; Integrity assessment
KW  -  Object detection
TI  - Deep Learning Object Detection Techniques for Thin Objects in Computer Vision: An Experimental Investigation
SP  - 295
ID  - scholars15013
ER  -