dc.contributor.author |
Singh, Navin |
|
dc.date.accessioned |
2024-02-20T08:36:11Z |
|
dc.date.available |
2024-02-20T08:36:11Z |
|
dc.date.issued |
2023 |
|
dc.identifier.uri |
https://ieeexplore.ieee.org/abstract/document/10389186 |
|
dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/14367 |
|
dc.description.abstract |
This paper presents a comparative study on three Convolutional Neural Network (CNN) object detection algorithms to find the best detector based on the combination of speed and accuracy on a personal computer. The MATLAB® development environment is used to evaluate three different object detector algorithms, namely Faster Region-Based Convolutional Network (R-CNN), Single Shot Detector (SSD) and You Only Look Once (YOLO). These algorithms are trained, and their performance metrics are tested on a small sample dataset. The results show that the SSD object detector algorithm performs best when considering both performance and processing speeds. Faster R-CNN detected objects at an average speed of 4.838 seconds and achieved a mean average precision of 0.76 with an average loss of 0.429. SSD detected objects at an average speed of 0.377 seconds and achieved a mean average precision of 0.92 with an average loss of 1.754. YOLO v3 detected objects at an average speed of 1.004 seconds and achieved a mean average precision of 0.81 with an average loss of 2.739. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.subject |
Physics |
en_US |
dc.subject |
Convolutional neural network (CNN) |
en_US |
dc.subject |
Object detection |
en_US |
dc.subject |
Computer Vision |
en_US |
dc.subject |
Image preprocessing |
en_US |
dc.subject |
MATLAB |
en_US |
dc.title |
Comparative Study of Convolutional Neural Network Object Detection Algorithms for Image Processing |
en_US |
dc.type |
Article |
en_US |