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About the application:

Easy Mobile - AI Image Enhancer and Detection. AI Image Enhancer and Detection brings you all the object detection and recognition in images, as well as fashionable and easy-to-use apks in Android device system. Browse, manage, crop, and edit images or videos faster than ever, recover accidentally deleted files or make hidden vaults for your most valuable photos and videos. With advanced file help and complete customization, your media library works as you wish. Advanced photo detection and recognition device. Object Detection - As the name suggests, it is the detection of objects in photos or videos through deep learning algorithms. The goal of object detection is to identify and localize all known objects in a stage. With this identification and localization, object detection can be used to count objects in a stage, determine and track their precise place, and at the same time accurately label them. Object detection is often confused with photo recognition, so before we proceed, it is necessary to clarify the difference between them.Image recognition assigns a label to an photo. Pictures of dogs are tagged with "dog". Images of the two dogs will still be labeled "dog." Object detection, on the another hand, draws a box around each dog and labels this box "dog". The model predicts where each object is and what label could be applied. In this method, object detection provides more info about the photo than recognition. Target detection realizes the classification task of the target in the photo and the bounding box position frame detection task. YOLOv3 is the third ver of the YOLO algorithm in the target detection algorithm. YOLOv3 uses a separate neural network to act on the photo, divides the photo into multiple regions and predicts the bounding box and the probability of each region. YOLOV3 is a network structure in the YOLO network series, so the idea of ​​the algorithm is a one-stage way (one scene), which expresses the detection task as a unified, end-to-end regression trouble, and only processes the photo once to receive the position and Classification. Compared with YOLOv2, the largest improvement of YOLOv3 is that it draws on the multi-scale discrimination of SSD, that is, predicts on feature maps of various sizes. For the large-size feature maps of the first few layers of the network, little objects can be effectively detected, and for the small-size feature maps at the end of the network, really big objects can be effectively detected. In addition, the backbone of YOLOv3 chose the DarkNet53 network, which has a deeper network structure and stronger feature extraction capabilities.