One of the most effective ways to test and debug your image object detection algorithms and systems is to visualize your results and errors. You can use tools like Matplotlib, OpenCV, or PIL to ...

Please note that the test images used in this competition is independent from those released as part of the Open Images Dataset. ... Using FasterRCNN+InceptionResNet V2, an SSD-based object detection ...

Update: This README and Repository is now fully updated for Tensorflow 2. If you want to use Tensorflow 1 instead check out my article. If you want to train your model in Google Colab check out the ...

Researchers have developed a new high-speed way to detect the location, size and category of multiple objects without acquiring images or requiring complex scene reconstruction. Because the new ...

Current methods for salient object detection in optical remote sensing images (RSI-SOD) adhere strictly to the conventional supervised train-test paradigm, where models remain fixed after training and ...

New adversarial techniques developed by engineers can make objects 'invisible' to image detection systems that use deep-learning algorithms. These techniques can also trick systems into thinking ...

The development of robotic avatars could benefit from an improvement in how computers detect objects in low-resolution images Just making a small tweak to algorithms typically used to enhance images ...