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Simple Mobile - AI Photo Enhancer and Detection.
AI Photo Enhancer and Detection brings you all the object detection and recognition in photos, as well as fashionable and easy-to-use applications in Android system. Browse, manage, crop, and edit photos or videos faster than ever, recover accidentally deleted files or create hidden vaults for your most valuable images and videos. With advanced file support and complete customization, your media library works as you want. Advanced image detection and recognition device.
Object Detection - As the name suggests, it is the detection of objects in images or videos through deep learning algorithms. The goal of object detection is to identify and localize all known objects in a scene. With this identification and localization, object detection can be used to count objects in a scene, determine and track their precise location, and at the same time accurately label them.
Object detection is often confused with image recognition, so before we proceed, it is important to clarify the difference between them.Image recognition assigns a label to an image. Pictures of dogs are tagged with "dog". Photos of the two dogs will still be labeled "dog." Object detection, on the other hand, draws a box around each dog and labels this box "dog". The model predicts where each object is and what label should be applied. In this way, object detection provides more information about the image than recognition.
Target detection realizes the classification task of the target in the image and the bounding box position frame detection task.
YOLOv3 is the third version of the YOLO algorithm in the target detection algorithm.
YOLOv3 uses a separate neural network to act on the image, divides the image 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 method (one stage), which expresses the detection task as a unified, end-to-end regression problem, and only processes the image once to obtain the position and Classification.
Compared with YOLOv2, the biggest improvement of YOLOv3 is that it draws on the multi-scale discrimination of SSD, that is, predicts on feature maps of different sizes. For the large-size feature maps of the first few layers of the network, small objects can be effectively detected, and for the small-size feature maps at the end of the network, large 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.