A newly proposed object detection method using Faster R-CNN inception with ResNet based on Tensorflow

Sujay Saha; Kanij Mehtanin Khabir; Shadman Saquib Abir; Ariful Islam

doi:10.1117/12.2523930

14 May 2019 A newly proposed object detection method using Faster R-CNN inception with ResNet based on Tensorflow

Sujay Saha, Kanij Mehtanin Khabir, Shadman Saquib Abir, Ariful Islam

Proceedings Volume 10996, Real-Time Image Processing and Deep Learning 2019; 109960X (2019) https://doi.org/10.1117/12.2523930
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

The purpose of the project is to study the previous methods of Object Detection using Deep Learning and propose a new method. The new model consists of three different techniques or processes: Regional Convolutional Neural Network (RCNN), Inception and ResNet. In object detection, the prime target of each method is to make sure maximum accuracy, high FPS, greater resolution and faster speed. But, due to the limitation of computational power it is not always possible to maintain a balance between these four. R-CNN in general is capable of handling high resolution images with a decent number of frames per second. In our method we introduced the concept of inception by dividing each large convolutional layer into smaller convolutional layers. And, by adding ResNet, we were able to get rid any extra layer that was not helping us in the gaining higher accuracy. Though we could achieve a low FPS, but the input image size was high resolution and the mean average precision was also high (almost close to SSD). We retrained the COCO and OpenImage datasets and results were decent enough. The model we build was based on the TensorFlow library.

Citation Download Citation

Sujay Saha, Kanij Mehtanin Khabir, Shadman Saquib Abir, and Ariful Islam "A newly proposed object detection method using Faster R-CNN inception with ResNet based on Tensorflow", Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 109960X (14 May 2019); https://doi.org/10.1117/12.2523930

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