Enhanced Scaling Object Detection to the Edge with YOLOv4, TensorFlow Lite and EEG
Keywords:
Computer Vision, Neuroscience Technologies, Electroencephalography (EEG), YOLOv4, TensorFlow, Object Identification, Real-time EEG Data, Neural Signatures, Human Emotions, Cerebral Impulses, Perceptible Visual Cues, Detection Framework, Deep Learning Architecture, Symbiotic Link, Mind-Technology Interface, Human-Computer Interaction, Emotional Reactions, Cutting-edge Technology, Novel Method, Seamless ConversionAbstract
A state-of-the-art marriage of computer vision and neuroscience technologies. We want to revolutionize how we interact with and comprehend human emotions by combining Electroencephalography (EEG) with on-device object identification driven by YOLOv4 and TensorFlow. We tap into the complex network of cerebral impulses through the gathering of real-time EEG data, capturing the core of human emotions as they emerge in the brain. Our object identification algorithm is triggered by these neural signatures, which enables a seamless conversion of feelings into perceptible visual cues. Our detection framework's foundation is YOLOv4, a cutting-edge deep learning architecture renowned for its accuracy and effectiveness in object identification. The strong basis is provided by TensorFlow, ensuring smooth integration and top performance. We aim to establish a symbiotic link between the mind and technology by utilizing the strength of both EEG and object detection. This novel method opens a wide range of possibilities, from improving human-computer interaction to offering priceless insights on emotional reactions in many contexts.
Downloads
References
Praneeth, Renduchinthala Sai, et al. "Scaling Object Detection to the Edge with YOLOv4, TensorFlow Lite." 2023 7th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2023.
Li, Rui, Le-Dian Liu, and Baa-Liang Lu. "Measuring human decision confidence from EEG signals in an object detection task." 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021.
Mohedano, Eva, et al. "Exploring EEG for object detection and retrieval." Proceedings of the 5th ACM international conference on multimedia retrieval. 2015.
Duan, Yiping, et al. "EEG-based maritime object detection for IoT-driven surveillance systems in smart ocean." IEEE Internet of Things Journal 7.10 (2020): 9678-9687.
Khasawneh, Natheer, Mohammad Fraiwan, and Luay Fraiwan. "Detection of K-complexes in EEG signals using deep transfer learning and YOLOv3." Cluster Computing (2022): 1-11.
Singh, Aditya, et al. "Understanding EEG signals for subject-wise definition of armoni activities." arXiv preprint arXiv:2301.00948 (2023).
Simanova, Irina, et al. "Identifying object categories from event-related EEG: toward decoding of conceptual representations." PloS one 5.12 (2010): e14465.
Jansen, Ben H., and Benoit M. Dawant. "Knowledge-based approach to sleep EEG analysis-a feasibility study." IEEE Transactions on Biomedical Engineering 36.5 (1989): 510-518.
Mohedano, Eva, et al. "Object segmentation in images using EEG signals." Proceedings of the 22Nd ACM international conference on multimedia. 2014.
Chambon, Stanislas, et al. "A deep learning architecture to detect events in EEG signals during sleep." 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2018.
FREDERICK, ALLAN, and KANISHKA MITRA. "Combining Object Detection with EEG for Ubiquitous BMI." (2023).
Xing, Le, and Alex Casson. "Deep autoencoder for real-time single-channel EEG cleaning and its smartphone implementation using TensorFlow Lite with hardware/software acceleration." (2023).
Brown, J. Teye, and Walid Zgallai. "Deep EEG: Deep learning in biomedical signal processing with EEG applications." Biomedical Signal Processing and Artificial Intelligence in Healthcare. Academic Press, 2020. 113-151.
Van Vliet, Marijn, et al. "Designing a brain-computer interface-controlled videogame using consumer grade EEG hardware." 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC). IEEE, 2012.
Ewing, Kate C., Stephen H. Fairclough, and Kiel Gilleade. "Evaluation of an adaptive game that uses EEG measures validated during the design process as inputs to a biocybernetic loop." Frontiers in human neuroscience 10 (2016): 223.
Astolfi, Laura, et al. "Imaging the social brain: multi-subjects EEG recordings during the “Chicken's game”." 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE, 2010.
Badcock, Nicholas A., et al. "Validation of the Emotiv EPOC® EEG gaming system for measuring research quality auditory ERPs." PeerJ 1 (2013): e38.
Stein, Adi, et al. "EEG-triggered dynamic difficulty adjustment for multiplayer games." Entertainment computing 25 (2018): 14-25.
Belkacem, Abdelkader Nasreddine, et al. "Real-time control of a video game using eye movements and two temporal EEG sensors." Computational intelligence and neuroscience 2015 (2015): 1-1.
Vasiljevic, Gabriel Alves Mendes, and Leonardo Cunha De Miranda. "Brain–computer interface games based on consumer-grade EEG Devices: A systematic literature review." International Journal of Human–Computer Interaction 36.2 (2020): 105-142
Yoon, Giha, et al. "Implementing Practical DNN-Based Object Detection Offloading Decision for maximizing Detection Performance of Mobile Edge Devices." IEEE Access 9 (2021): 140199-140211.
Wu, Yujie, et al. "Video Object Detection Guided by Object Blur Evaluation." IEEE Access 8 (2020): 208554-208565.
Park, Hyeseung, Seungchul Park, and Youngbok Joo. "Detection of abandoned and stolen objects based on dual background model and mask R-CNN." IEEE Access 8 (2020): 80010-80019.
Tao, Ye, et al. "Low-altitude small-sized object detection using lightweight feature-enhanced convolutional neural network." Journal of Systems Engineering and Electronics 32.4 (2021): 841-853.
Sallang, Nicholas Chieng Anak, et al. "A CNN-Based Smart Waste Management System Using TensorFlow Lite and LoRa-GPS Shield in the Internet of Things Environment." IEEE Access 9 (2021): 153560-153574.
Zheng, Ziyang, et al. "Arbitrarily Oriented Object Detection in Remote Sensing Images Based on Improved
Marshall, M. R., et al. "3-D object tracking in panoramic video and LiDAR for radiological source–object attribution and improved source detection." IEEE Transactions on Nuclear Science 68.2 (2020): 189-202.
Fu, Lanhui, et al. "Fast and accurate detection of banana fruits in complex background orchards." IEEE Access 8 (2020): 196835-196846.
de Oliveira, Bernardo Augusto Godinho, Flávia Magalhães Freitas Ferreira, and Carlos Augusto Paiva da Silva Martins. "Fast and lightweight object detection network: Detection and recognition on resource-constrained devices." IEEE Access 6 (2018): 8714-8724.
Saini, Manali, and Udit Satija. "On-Device Implementation for Deep-Learning-Based Cognitive Activity Prediction." IEEE Sensors Letters 6.4 (2022): 1-4.
Kumar, Shailender, Pranav Sharma, and Nitin Pal. "Object tracking and counting in a zone using YOLOv4, DeepSORT, and TensorFlow." 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, 2021.
Noor, Wan Emilya Izzety Binti Wan, and Naimah Mat Isa. "Object Detection: Harmful Weapons Detection using YOLOv4." 2021 IEEE Symposium on Wireless Technology & Applications (ISWTA). IEEE, 2021.
Howell, Lewis, Vasileios Anagnostidis, and Fabrice Gielen. "Multi‐Object Detector YOLOv4‐Tiny Enables High‐Throughput Combinatorial and Spatially Resolved Sorting of Cells in Microdroplets." Advanced Materials Technologies 7.5 (2022): 2101053.
Kuntal, Tilak Singh, and Mrinal Paliwal. "OBJECT DETECTION USING YOLOV4."
Wu, Han, et al. "SORT-YM: An algorithm of multi-object tracking with YOLOv4-tiny and motion prediction." Electronics 10.18 (2021): 2319.
Saponara, Sergio, Abdussalam Elhanashi, and Qinghe Zheng. "Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19." Journal of Real-Time Image Processing 19.3 (2022): 551-563.
Talele, Ajay, Aseem Patil, and Bhushan Barse. "Detection of real time objects using TensorFlow and OpenCV." Asian Journal for Convergence in Technology (AJCT) ISSN-2350-1146 (2019).
Dadhich, Abhinav. Practical Computer Vision: Extract Insightful Information from Images Using TensorFlow, Keras, and OpenCV. Packt Publishing Ltd, 2018.
Jocher, Glenn, et al. "ultralytics/yolov5: v6. 0-YOLOv5n'Nano'models, Roboflow integration, TensorFlow export, OpenCV DNN support." Zenodo (2021).
Baruah, Abhishika, and A. B. Kandali. "Pedestrian Detection Based on OpenCV and TensorFlow." International Journal of Research and Analytical Reviews 5.2 (2018): 1218-1221.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Renduchinthala Sai Praneeth Kumar, Kancharla Chetan Sai Akash, Bommisetty Keerthi Sree, P. Ithaya Rani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
