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Introduction II. Development Environment III. Project Construction 3. Raw Blame. Edit this file. Open with Desktop View raw View blame. Introduction This project will develop and compile the source code of Yolo-v3 in android studio and deploy it to android mobile phone to realize the call of Yolo-v3 network by using mobile phone hardware platform. Development Environment OS: Ubuntu Project operation Run the project on Xiaomi 8, you can see the file information read in the logcat of android studio 3, and the configuration of Yolo-v3-tiny network.

Logcat will print out the predicted object name, probability and processing time. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. This repo will also be updated regularly. The detector function in AlexeyAB Darknet only supports a single image at a time. Therefore I added the batch function into this forked repo, which supports detecting images in a folder in one time.

In the meantime, it exports information including the name of the image, the detected classes, the confidence and the bounding box coordinates in JSON and TXT files. Figure 1. Example of Object Detection using Yolo based on the Darknet. Batch images detector Figure. The process of batch detecting images in a folder using Yolo based on the Darknet.

Hope you like it. Compile without change anything on Linux and Windows. Both are tested. Export the bounding box of detected objects in images to JSON. Export the bounding box of detected objects in images to TXT.

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