Implementasi Algoritma YOLO pada Aplikasi Pendeteksi Senjata Tajam di Android. - Christopher Nathanael Liunanda; Silvia Rostianingsih; Anita Nathania Purbowo (Jurnal Infra, 2020)
Advances in computing power have exceeded traditional smartphone needs. Object detection requires high computational power. Tensorflow Lite can be used to run models quickly and easily to mobile devices. The YOLO network was used because of faster and more accurate performance than other similiar networks. The object to be identified are bladed weapons that are knifes and machetes. Bladed weapons are selected because of potential applications in the real world.The trained models are YOLOv2-tiny, YOLOv3-tiny and YOLOv3. Transfer Learning is done to these models with Darknet so that YOLO can detect the desired weapon. Darknet model will be converted to Tensorflow Lite. Model testing is done by looking at some standard accuracy metrics such as precision, recall, mAP, and the average IoU. The model with the best performance will be installed in the Android application to detect bladed weapon objects knifes and machetes.The test results show that the performance of the model is very dependent on the type of network, the number of datasets, and the shape of the dataset. YOLOv2-tiny produces the worst result with mAP of 55% and average IoU of 35%. The final accuracy for Tensorflow Lite Android model are 72.7% for YOLOv3 and 63.6% for YOLOv3-tiny. The YOLOv3-tiny network is suitable for real-time detection because of fast inference time (0.9 seconds).