Convolutional Neural Network for Object Detection System for Blind People

Y.C. Wong, J.A. Lai, S.S.S. Ranjit, A.R. Syafeeza, N. A. Hamid


Blind or visually impaired people are usually unaware of the danger that they are facing in their daily life. They may face many challenges when performing their daily activity even in familiar environments. This work proposed a smart object detection system based on Convolutional Neural Network (CNN) to provide a smart and safe living for visually impaired people. To reduce the complexity load, region proposals from the edge maps of each image were produced using edge box algorithm. Then, the proposals passed through a fine-tuned CaffeNet model. The object scene was captured by the webcam in real time and the feature of the image was extracted. After that, audio-based detector was generated on the detected object to notify the visually impaired people about the identified object. The result was evaluated by using mean average precision (mAP) and frame-per-second and it was found that the Single Shot MultiBox Detector (SSD) reduces the complexity and achieves higher accuracy as well as faster speed in object detection compared to Fast R-CNN.


Blind, Cloud, Object Detection, Object Recognition, Image Processing;

Full Text:



T. Guo, J. Dong, H. Li and Y. Gao, "Simple Convolutional Neural Network on Image Classification," IEEE 2nd International Conference on Big Data Analysis (ICBDA), 2017, pp. 721-724.

Y. C. Wong, Y. Q. Lee, " Design and Development of Deep Learning Convolutional Neural Network on an Field Programmable Gate Array, Journal of Telecommunication, Electronic and Computer Engineering, 2018, pp. 25-29.

A. Rosebrock, "Real-time object detection with deep learning and OpenCV", [Online]. Availalable: /2017/09/18/real -time-object-detection-with-deep-learning-andopencv/. [Accessed: 16-December-2018].

P. Viola, M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features", Computer Vision and Pattern Recognition, 2001, pp. 1-9.

S. Tang, "Object Detection based on Convolutional Neural Network," [Online]. Available: [Accessed: 16- April-2019]

T. Guo, J. Dong, H. Li and Y. Gao, "Simple Convolutional Neural Network on Image Classification," IEEE 2nd International Conference on Big Data Analysis (ICBDA), 2017, pp. 721-724.

I. M. Gorovyi and D. S. Sharapov, "Comparative Analysis of Convolutional Neural Networks and Support Vector Machines for Automatic Target Recognition", IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS), Kiev, 2017, pp. 63-66.

S. S. Liew, M. Khalil-hani, and S. A. Radzi, "Gender classification : a convolutional neural network approach", Turkish Journal of Electrical Engineering and Computer Sciences, vol. 24, no. 3, 2016, pp. 1248– 1264.

B. S. Hijazi, R. Kumar, C. Rowen, and I. P. Group, "Using Convolutional Neural Networks for Image Recognition", [Online]. Availalable: [Accessed: 16-April-2019]

K. Alex, N. Vinod and H. Geoffrey, "CIFAR-10 and CIFAR-100 datasets", [Online]. Availalable:, [Accessed: 22- November-2018]

S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," NIPS, 2015, pp. 91–99.

C. R. Joseph, "YOLO: Real-Time Object Detection", [Online]. Availalable:, [Accessed: 5 January 2018]

B. V. Core, "Energy Efficient Computing : A Comparison of Raspberry PI with Modern Devices Devices included Energy And Performance Evaluation Comparision : Computing Energy Efficiency : Conclusion", 2012, pp. 6–7.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131