Open AccessAn object detection method for aerial hazy images
Author(s) -
Minh Triet Tran,
Bao V. Tran,
Nguyen D. Vo,
Khang Nguyen
Publication year - 2022
Publication title -
can tho university journal of science
Language(s) - English
Resource type - Journals
eISSN - 2815-5602
pISSN - 2615-9422
DOI - 10.22144/ctu.jen.2022.010
Subject(s) - haze , artificial intelligence , computer science , computer vision , object detection , benchmark (surveying) , object (grammar) , aerial image , feature (linguistics) , image (mathematics) , pattern recognition (psychology) , geography , linguistics , philosophy , geodesy , meteorology
Image processing and object detection in aerial images have to deal with a lot of trouble due to the existence of haze, smoke, dust in the atmosphere. These factors can blur objects and severely decline image quality which might lead to incorrect or missing object detection. To solve this problem, this study shows a method that can reduce the bad effect of haze on object detection in aerial images. A combination of a dehazing method called Feature Fusion Attention Network (FFA-Net) and an object detection method named Probabilistic Anchor Assignment (PAA) was conducted to evaluate two hypotheses: (1) haze was a noisy factor and (2) haze was treated as part of objects. Through extensive experiments, the selective dehazing hypothesis, which was used for truck objects, improved the detection result of car and bus from 19.6% to 21.9% and 0.7% to 4.4%, respectively, on the UAVDT-Benchmark-M dataset. This result showed that our approach was effective.