Logo Detection Using Deep Learning

Logo detection plays a pivotal role in various applications like brand monitoring, image retrieval, and counterfeit detection. This paper presents a comprehensive comparative study among three state-of-the-art methods: YOLOv9, YOLOv8, and CNN-VGG19. YOLOv9 and YOLOv8 are CNN-based object detection frameworks known for real-time capabilities, while CNN-VGG19 excels at extracting intricate features from images. We evaluated and compared their performance on a diverse logo dataset encompassing varying environmental conditions and logo types. Metrics such as detection accuracy, speed, and robustness to environmental variations were analyzed. Our experimental results demonstrate that all three methods achieved strong capabilities for logo detection tasks, each with distinct advantages. Notably, we achieved an impressive overall accuracy of 94.20%. However, CNN-VGG19 stood out in detecting logos under challenging conditions and extracting fine-grained features. This makes it preferable for applications demanding precise logo recognition, such as counterfeit detection and brand monitoring in cluttered scenes. The findings of this study, including the benchmark of 94.20% accuracy, provide valuable insights for selecting the most suitable logo detection method based on specific application requirements.