Smart System Identifikasi Objek Menggunakan Kamera Esp32 dan OpenCV

Abstract

Technology used to help complete work is a must. In Buluroto Village, Blora there are 5 hamlets with 6357 residents. There are still many places with poor lighting, so theft still occurs frequently. Ordinary CCTV has difficulty distinguishing objects because of poor lighting, so a system is needed to identify objects with poor lighting. This system was created using Prototype development method with Esp32 Camera, Micro-USB Cable, Esp32-MB, and OpenCV. It was concluded that the smart object identification system was able to detect 20 objects in one frame and store 80 objects in the library. This system can work optimally with an accuracy level reaching 50% - 98%. The delay test results showed average time was 2.18 seconds and around 87.17% of random respondents agreed with this system because it can identify objects like humans, animals, moving and immovable objects well so it is hoped can increase the level of environmental safety.