چکیده:
Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs convolution operations on images. The real time object detection delivers an accuracy of 92.7%, which is an improvement over some of the existing models already proposed earlier. Model detects hundreds of objects simultaneously. In the proposed model, accuracy of object detection significantly improvises over existing methodologies in practice. There is a substantial dataset to evaluate the accuracy of proposed model. The model may be readily useful for object detection applications including parking lots, human identification, and inventory management.
خلاصه ماشینی:
Real Time Object Detection using CNN based Single Shot Detector Model Abhinav Juneja * *Corresponding Author, Professor, Department of IT, KIET Group of Institutions, Delhi-NCR Ghaziabad, Uttar Pradesh, India.
Object Detection; Deep Learning; CNN; SSD; Tensor Flow; OpenCV / DOI: 10.
Object detection is a multi-disciplinary research area; it often involves fields of image processing, deep learning and computer vision.
In our current experimental work, we have used the concepts of machine learning and computer vision for object detection in real time environment.
CNN (Convolution Neural Network): CNN is a specialized category of the deep learning algorithm that may accept as an input some sample image and perform convolution operation to extract features from an input image and be able to differentiate each object from one other.
Classification Network using Convolutional layer R-CNN: To tackle the limitation due to requirement for selecting a huge number of regions, researcher Ross Girshick illustrated a technique incorporating the selective search methods for extracting ‘2000’ regions of the image which are addressed as the region proposals (Ren, He, Girshick, & Sun, 2017).
SSD(Single Shot Multi-Box Detector): This method is popular for application in object detection (Liu et al.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.
Object detection networks on convolutional feature maps.
MobileNet-Tiny: A deep neural network-based real-time object detection for rasberry Pi. Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, 647–652.
Real Time Object Detection using CNN based Single Shot Detector Model.