Voice Alert Sebagai Alat Bantu Penglihatan di Lingkungan Rumah dan Jalanan Secara Umum Berbasis Android

Kevin Christian Salim(1*), Liliana Liliana(2), Rolly Intan(3),


(1) Program Studi Informatika
(2) Program Studi Informatika
(3) Program Studi Informatika
(*) Corresponding Author

Abstract


According to research conducted by the Governors Highway Safety Assocation in 2017 showed that there are about 6 thousand pedestrians killed in America due to the habit of using smartphones while walking. Another study from Jeff Ronsen showed that the brain is overloaded and cannot function properly when performing these two activities at the same time. Walking while using a smartphone makes the concentration on the atmosphere of the road split and makes pedestrians not focus on the road but rather on the smartphone. Such behavior results in an increased risk of pedestrian accidents. One solution to prevent accidents above is to use the smartphone camera to take pictures in front of the user.

Smartphone cameras can be used to retrieve input data in the form of images in real time which is then carried out the process of object detection and issue alerts in the form of sounds that mention the name of the detected object to smartphone users. Detected objects are objects that are generally located in home and street environments such as humans, cars, bicycles, motorcycles, and stop signs. Object detection using SSD MobileNet applied transfer learning that is further trained by using google open image dataset v6 dataset. The result of transfer learning is weight used to detect objects from android camera input.

The test results showed that SSD_MobileNet_V2 with a learning rate of 0.01 and steps 10,000 has the best mAP value with 80% in detecting objects. The SSD_MobileNet_V2 can detect objects with an inference time speed of 80ms – 110ms in real time in a standby device, and voice alerts by instantly issuing alerts when an object is detected.


Keywords


SSD; MobileNet; Voice Alert; Android; Real Time Object Detection

Full Text:

PDF

References


Ambeth Kumar, V.D. 2018. Precautionary measures for accidents due to mobile phone using IOT. Clinical eHealth. 1, 1 (2018), 30–35. DOI:doi.org/10.1016/j.ceh.2018.12.001.

Biswas, D., Su, H., Wang, C., Stevanovic, A. and Wang, W. 2019. An automatic traffic density estimation using Single Shot Detection (SSD)and MobileNet-SSD. Physics and Chemistry of the Earth. 110, (2019), 176–184. DOI:doi.org/10.1016/j.pce.2018.12.001.

Botzer, A., Musicant, O. and Perry, A. 2017. Driver behavior with a smartphone collision warning application – A field study. Safety Science. 91, (2017), 361–372. DOI:doi.org/10.1016/j.ssci.2016.09.003.

Cao, M.T., Tran, Q.V., Nguyen, N.M. and Chang, K.T. 2020. Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources. Advanced Engineering Informatics. 46, April (2020), 101182. DOI:doi.org/10.1016/j.aei.2020.101182.

Chen, P.L. and Pai, C.W. 2018. Pedestrian smartphone overuse and inattentional blindness: An observational study in Taipei, Taiwan. BMC Public Health. 18, 1 (2018), 1–10. DOI:doi.org/10.1186/s12889-018-6163-5.

Crowley, P., Madeleine, P. and Vuillerme, N. 2019. The effects of mobile phone use on walking: A dual task study. BMC Research Notes. 12, 1 (2019), 1–6. DOI:doi.org/10.1186/s13104-019-4391-0.

Feld, J.A. and Plummer, P. 2019. Visual scanning behavior during distracted walking in healthy young adults. Gait and Posture. 67, August 2018 (2019), 219–223. DOI:doi.org/10.1016/j.gaitpost.2018.10.017.

Gievska, S. and Madjarov, G. 2019. Detection of Toy Soldiers Taken from a Bird’s Perspective Using Convolutional Neural Networks.

Hamazima, M., Murayama, T., Yamasaki, H., Nakano, T. and Yamada, M. 2020. Study on Simultaneous-Action Discrimination Method Using Deep Learning. International Journal of Intelligent Transportation Systems Research. (2020). DOI:doi.org/10.1007/s13177-019-00216-y.

Hedlund, J. 2017. Pedestrian Traffic Fatalities by State. Governors Highway Safety Assocation. 1, (2017).

Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M. and Adam, H. 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv. (2017).

Jeff Rossen 2017. Rossen to the Rescue: Secrets to Avoiding Scams, Everyday Dangers, and Major Catastrophes. Flatiron Books.

Kang, H.-J. 2020. Real-Time Object Detection on 640x480 Image With VGG16+SSD. (2020), 419–422. DOI:doi.org/10.1109/icfpt47387.2019.00082.

Kujala, T. and Mäkelä, J. 2018. Naturalistic study on the usage of smartphone applications among Finnish drivers. Accident Analysis and Prevention. 115, November 2017 (2018), 53–61. DOI:doi.org/10.1016/j.aap.2018.03.011.

Kumar, A. and Srivastava, S. 2020. Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector. Procedia Computer Science. 171, 2019 (2020), 2610–2617. DOI:doi.org/10.1016/j.procs.2020.04.283.

Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., Duerig, T. and Ferrari, V. 2020. The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale. International Journal of Computer Vision. 128, 7 (2020), 1956–1981. DOI:doi.org/10.1007/s11263-020-01316-z.

Li, J., Hou, Q., Xing, J. and Ju, J. 2020. SSD Object Detection Model Based on Multi-Frequency Feature Theory. IEEE Access. 8, (2020), 82294–82305. DOI:doi.org/10.1109/ACCESS.2020.2990477.

Li, Y., Dong, H., Li, H., Zhang, X., Zhang, B. and Xiao, Z. 2020. Multi-block SSD based on small object detection for UAV railway scene surveillance. Chinese Journal of Aeronautics. 33, 6 (2020), 1747–1755. DOI:doi.org/10.1016/j.cja.2020.02.024.

Lin, M.I.B. and Huang, Y.P. 2017. The impact of walking while using a smartphone on pedestrians’ awareness of roadside events. Accident Analysis and Prevention. 101, (2017), 87–96. DOI:doi.org/10.1016/j.aap.2017.02.005.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y. and Berg, A.C. 2016. SSD: Single Shot MultiBox Detector Wei. ECCV. 1, (2016), 398–413. DOI:doi.org/10.1007/978-3-319-46448-0_2.

Liu, Y. 2018. An Improved Faster R-CNN for Object Detection. Proceedings - 2018 11th International Symposium on Computational Intelligence and Design, ISCID 2018. 2, (2018), 119–123. DOI:doi.org/10.1109/ISCID.2018.10128.

Luque, A., Carrasco, A., Martín, A. and de las Heras, A. 2019. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition. 91, (2019), 216–231. DOI:doi.org/10.1016/j.patcog.2019.02.023.

Mourra, G.N., Sénécal, S., Fredette, M., Lepore, F., Faubert, J., Bellavance, F., Cameron, A.F., Labonté-LeMoyne, É. and Léger, P.M. 2020. Using a smartphone while walking: The cost of smartphone-addiction proneness. Addictive Behaviors. 106, August 2019 (2020), 106346. DOI:doi.org/10.1016/j.addbeh.2020.106346.

Novac, O.C., Novac, M., Gordan, C., Berczes, T. and Bujdoso, G. 2017. Comparative study of Google Android, Apple iOS and Microsoft Windows Phone mobile operating systems. 2017 14th International Conference on Engineering of Modern Electric Systems, EMES 2017. (2017), 154–159. DOI:doi.org/10.1109/EMES.2017.7980403.

Pinto de Aguiar, A.S., Neves dos Santos, F.B., Feliz dos Santos, L.C., de Jesus Filipe, V.M. and Miranda de Sousa, A.J. 2020. Vineyard trunk detection using deep learning – An experimental device benchmark. Computers and Electronics in Agriculture. 175, May (2020), 105535. DOI:doi.org/10.1016/j.compag.2020.105535.

Sai Srinath, N.G.S., Joseph, A.Z., Umamaheswaran, S., Priyanka, C.L., Malavika Nair, M. and Sankaran, P. 2020. NITCAD - Developing an object detection, classification and stereo vision dataset for autonomous navigation in Indian roads. Procedia Computer Science. 171, 2019 (2020), 207–216. DOI:doi.org/10.1016/j.procs.2020.04.022.

Sarkar, A., Goyal, A., Hicks, D., Sarkar, D. and Hazra, S. 2019. Android Application Development: A Brief Overview of Android Platforms and Evolution of Security Systems. Proceedings of the 3rd International Conference on I-SMAC IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2019. (2019), 73–79. DOI:doi.org/10.1109/I-SMAC47947.2019.9032440.

Shakeel, M.F., Bajwa, N.A., Anwaar, A.M., Sohail, A., Khan, A. and Haroon-ur-Rashid 2019. Detecting Driver Drowsiness in Real Time Through Deep Learning Based Object Detection. Springer International Publishing.

Sheng, T.J., Islam, M.S., Misran, N., Baharuddin, M.H., Arshad, H., Islam, M.R., Chowdhury, M.E.H., Rmili, H. and Islam, M.T. 2020. An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model. IEEE Access. 8, (2020), 148793–148811. DOI:doi.org/10.1109/ACCESS.2020.3016255.

Späth, P. 2018. Services. Pro Android With Kotlin. Apress, Berkeley, CA. 27–42.

Sutjiadi, R. and Pattiasina, T.J. 2020. Deteksi Objek Menggunakan Dashboard Camera Untuk Sistem Peringatan Pencegah Kecelakaan Pada Mobil. 7, 2 (2020), 427–434. DOI:doi.org/10.25126/jtiik.202072520.

Xu, M., Lin, F.X., Liu, J., Liu, Y., Liu, Y. and Liu, X. 2019. A first look at deep learning apps on smartphones. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. 2018, (2019), 2125–2136. DOI:doi.org/10.1145/3308558.3313591.


Refbacks

  • There are currently no refbacks.


Jurnal telah terindeks oleh :