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This paper presents the development of a new algorithm in the field of image processing that enables the detection of flood disasters quickly and accurately, using the SONIC (Smart water indication optimizer) method. Concentrated detection in online real-time camera systems has been performed by several tests consisting of classifying camera objects, thermal cameras, and learning cameras. The introduction to the RTC web enables real-time and multiplatform data delivery systems on devices comprised of computers and android gadgets, on object classification using the SONIC algorithm. The object consists of humans, yellow balls, and green balls, with each sample having 50 points of view. The experiments showed test results up to 100% per age with real-time camera capture speeds.

Foreign object damage (FOD) adalah persoalan besar pada industri penerbangan yang berpengaruh pada tingkat keselamatan. Pesawat terkadang kehilangan bagian kecil ketika mendarat. Deteksi objek FOD pada MobileNetV2 serta menjadi backend pada proses pengujian model. Pada pengujian didapatkan bahwa nilai probabilitas dari pendeteksian berkisar diatas 52%, dihasilakan bahwa model dapat digunakan data dengan baik pada perubahan intensitas cahaya pada kamera.

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