Contrast Enhancement-Based Preprocessing Process to Improve Deep Learning Object Task Performance and Results

Excessive lighting or sunlight can make it difficult to judge visually. The same goes for cameras that function like the human eye. In the field of computer vision, object tasks have a significant impact on performance depending on how much object information is provided. Light presents difficulties...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied sciences 2023-10, Vol.13 (19), p.10760
Hauptverfasser: Wang, Tae-su, Kim, Gi Tae, Kim, Minyoung, Jang, Jongwook
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Excessive lighting or sunlight can make it difficult to judge visually. The same goes for cameras that function like the human eye. In the field of computer vision, object tasks have a significant impact on performance depending on how much object information is provided. Light presents difficulties in recognizing objects, and recognition is not easy in shadows or dark areas. In this paper, we propose a contrast enhancement-based preprocessing process to obtain improved results in object recognition tasks by solving problems that occur due to light or lighting conditions. The proposed preprocessing process involves the steps of extracting optimal values, generating optimal images, and evaluating quality and similarity, and it can be applied to the generation of training and input data. As a result of an experiment in which the preprocessing process was applied to an object task, the object task results for areas with shadows or low contrast were improved while the existing performance was maintained for datasets that require contrast enhancement technology.
ISSN:2076-3417
2076-3417
DOI:10.3390/app131910760