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@article{198842,
        author = {Ashwani  Kumar and Archana Jain and Pankaj Singh and Kuldeep Singh},
        title = {On device ai in flutter using Tflite},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15636-15647},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198842},
        abstract = {The possibility of intelligent visual systems being implemented directly on smartphones has been made possible with the increased adoption of mobile computing and artificial intelligence. Computer vision object detection and its automation of locating and identifying multiple objects in an image or video frame has a wide array of use cases such as assistive technology, education, surveillance, retail automation, and mobile scene understanding. Traditionally, mobile AI systems use a cloud-based method of inference, where visual data is sent from the mobile device to a remote server, where processing is performed. While this method provides powerful computing capabilities, there is an added latency, permanent internet connection is required, and visual data leaves the device, and with it the possibility of breaching the data’s privacy. To combat this, the processing of data on the device itself, as opposed to the remote server, provides a strong alternative for mobile applications that require optimum privacy and a near-instant response time.
This paper drafts a complete UGC-style journal on a privacy-preserving object detection system using Dart and Flutter on the app side and TensorFlow Lite on the inference side. The proposed system is designed to perform object detection on the user's smartphone using a lightweight mobile-friendly model. This approach eliminates the need for a mobile system to communicate with the cloud continuously, allowing for faster system response times. Given its capability for cross-platform development with a single codebase, Flutter was chosen for the front-end design. Additionally, TensorFlow Lite was selected for its optimized design to support on-device mobile machine learning inference. The paper addresses the research motivation, related work, and novelty, as well as system architecture, methodology, implementation approach, anticipated evaluation criteria, potential applications, system limitations, and future work. To aid in the final formatting, the paper also includes prompts for figures and diagrams to enhance the visuals of the manuscript.},
        keywords = {on-device AI, object detection, Flutter, TensorFlow Lite, mobile intelligence, privacy-preserving computing, edge AI, smartphone vision.},
        month = {April},
        }