A Case Study: Cat-Dog Face Detector Based on YOLOv5 - Cengil, Emine; Cinar, Ahmet; Yildirim, Muhammet (Institute of Electrical and Electronics Engineers Inc., 2021)
Object detection is a common research topic for many fields. In particular, objects that are close together are difficult to detect. The breed of cats and dogs includes many species. These species are similar to each other and to some species in the other class. Therefore, it is difficult to distinguish the faces of cats and dogs, especially for some species. The study uses the YOLO algorithms, which has very high sensitivity and speed in numerous object detection challenges. The Oxford pets dataset, consisting of approximately 3600 images, containing images from 37 different types of cat/dog classes, is utilized for training and testing. We propose a method based on YOLOv5 to find cats and dogs. We utilized the YOLOv5 algorithm with different parameters. Four different models are compared and evaluated. Experiments demonstrate that YOLOv5 models achieve successful results for the respective task. The mAP of YOLOv5l is 94.1, demonstrating the efficacy of YOLOv5-based cat/dog detection. © 2021 IEEE.
2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021 -- 29 September 2021 through 30 September 2021 -- Virtual, Online -- 173514
Keywords: cat and dog detection; Oxford pets dataset; YOLOv5