Research Article
Image Decluttering Techniques and Its Impact on YOLOv4 Performance
@INPROCEEDINGS{10.1007/978-3-031-17292-2_3, author={Maryam Asghari and Farshid Alizadeh-Shabdiz}, title={Image Decluttering Techniques and Its Impact on YOLOv4 Performance}, proceedings={Computer Science and Education in Computer Science. 18th EAI International Conference, CSECS 2022, On-Site and Virtual Event, June 24-27, 2022, Proceedings}, proceedings_a={CSECS}, year={2022}, month={11}, keywords={Image decluttering Data augmentation Face detection Image processing Image enhancement Object detection YOLO}, doi={10.1007/978-3-031-17292-2_3} }- Maryam Asghari
Farshid Alizadeh-Shabdiz
Year: 2022
Image Decluttering Techniques and Its Impact on YOLOv4 Performance
CSECS
Springer
DOI: 10.1007/978-3-031-17292-2_3
Abstract
Object detection and specifically face detection are challenging computer vision problems. The purpose of this study is to explore the effect of data augmentation and image decluttering technique on performance of YoloV4 model. In this work, we proposed the idea of image decluttering technique and evaluated its effect on the face detection. We have also investigated Mosaic augmentation technique and identified some drawbacks of using that and suggested an enhancement to the existing Mosaic augmentation to address the drawbacks and showed the impact of the new proposed mosaic augmentation technique on performance of face detection using YoloV4 model. This study is structured to find the effect of the proposed techniques on various images with diverse backgrounds, illumination, occlusions, and viewpoints. We achieved promising results that prove the effectiveness of the proposed techniques on detection probability, specifically in the challenging conditions.
