Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN
K G Shreyas Dixit1, Mahima Girish Chadaga2, Sinchana S Savalgimath3, G Ragavendra Rakshith4, Naveen Kumar M R5
1K G Shreyas Dixit, Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
2Mahima Girish Chadaga, Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
3Sinchana S Savalgimath, Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
4G Ragavendra Rakshith, Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
5Naveen Kumar M R, Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
Manuscript received on 21 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 824-829 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11540782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1154.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Object detection has boomed in areas like image processing in accordance with the unparalleled development of CNN (Convolutional Neural Networks) over the last decade. The CNN family which includes R-CNN has advanced to much faster versions like Fast-RCNN which have mean average precision(Map) of up to 76.4 but their frames per second(fps) still remain between 5 to 18 and that is comparatively moderate to problem-solving time. Therefore, there is an urgent need to increase speed in the advancements of object detection. In accordance with the broad initiation of CNN and its features, this paper discusses YOLO (You only look once), a strong representative of CNN which comes up with an entirely different method of interpreting the task of detecting the objects. YOLO has attained fast speeds with fps of 155 and map of about 78.6, thereby surpassing the performances of other CNN versions appreciably. Furthermore, in comparison with the latest advancements, YOLOv2 attains an outstanding trade-off between accuracy and speed and also as a detector possessing powerful generalization capabilities of representing an entire image.
Keywords: CNN, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, Image processing, Object Detection.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques