Depth sensing is an essential technology in robotics and many other fields. Many depth sensing (or RGB-D) cameras are available on the market and selecting the best one for your application can be challenging. In this work, we tested four stereoscopic RGB-D cameras that sense the distance by using two images from slightly different views. We empirically compared four cameras (Intel RealSense D435, Intel RealSense D455, StereoLabs ZED 2, and Luxonis OAK-D Pro) in three scenarios: (i) planar surface perception, (ii) plastic doll perception, (iii) household object perception (YCB dataset). We recorded and evaluated more than 3,000 RGB-D frames for each camera. For table-top robotics scenarios with distance to objects up to one meter, the best performance is provided by the D435 camera. For longer distances, the other three models perform better, making them more suitable for some mobile robotics applications. OAK-D Pro additionally offers integrated AI modules (e.g., object and human keypoint detection). ZED 2 is not a standalone device and requires a computer with a GPU for depth data acquisition. All data (more than 12,000 RGB-D frames) are publicly available at https://osf.io/f2seb.

Maritime obstacle detection is critical for safe navigation of autonomous surface vehicles (ASVs). While the accuracy of image-based detection methods has advanced substantially, their computational and memory requirements prohibit deployment on embedded devices. In this paper we analyze the currently best-performing maritime obstacle detection network WaSR. Based on the analysis we then propose replacements for the most computationally intensive stages and propose its embedded-compute-ready variant eWaSR. In particular, the new design follows the most recent advancements of transformer-based lightweight networks. eWaSR achieves comparable detection results to state-of-the-art WaSR with only 0.52% F1 score performance drop and outperforms other state-of-the-art embedded-ready architectures by over 9.74% in F1 score. On a standard GPU, eWaSR runs 10x faster than the original WaSR (115 FPS vs 11 FPS). Tests on a real embedded device OAK-D show that, while WaSR cannot run due to memory restrictions, eWaSR runs comfortably at 5.5 FPS. This makes eWaSR the first practical embedded-compute-ready maritime obstacle detection network. The source code and trained eWaSR models are publicly available here: https://github.com/tersekmatija/eWaSR.

In this tutorial, you will learn to train a custom image classification network for OAK-D using the TensorFlow framework. Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g., tomato, brinjal, and bottle gourd). If you are a regular PyImageSearch reader and have even basic knowledge of Deep Learning in Computer Vision, then this tutorial should be easy to understand. Furthermore, this tutorial acts as a foundation for the following tutorial, where we learn to deploy this trained image classification model on OAK-D. To learn how to train an image classification network for OAK-D, just keep reading. Before we start data loading, analysis, and training the classification network on the data, we must carefully pick the suitable classification architecture as it would finally be deployed on the OAK. Although OAK can process 4 trillion operations per second, it is still an edge device.

What's New: Artificial intelligence (AI) developer Jagadish K. Mahendran and his team designed an AI-powered, voice-activated backpack that can help the visually impaired navigate and perceive the world around them. The backpack helps detect common challenges such as traffic signs, hanging obstacles, crosswalks, moving objects and changing elevations, all while running on a low-power, interactive device. "Last year when I met up with a visually impaired friend, I was struck by the irony that while I have been teaching robots to see, there are many people who cannot see and need help. This motivated me to build the visual assistance system with OpenCV's Artificial Intelligence Kit with Depth (OAK-D), powered by Intel." Why It Matters: The World Health Organisation estimates that globally 285 million people are visually impaired.

In this last talk of day 01 of Computer Vision DevCon 2020, Brandon Gilles, CEO at Luxonis, explained the world's first embedded spatial AI platform -- OpenCV AI Kit (OAK). Embedded spatial AI platform provides an immense capability to imitate human-level perception in applications. This is essential for specific perception and interaction tasks that could conventionally be only solved by a person can now be performed by embedded systems. This talk starts with the explanation of OAK-1 and OAK-D and further explains how the OpenCV AI kit has two eyes to bring out the depth perception, especially the distance. Case in point -- the system can now quickly identify a good onion from a bad one on a conveyor belt and throw it back into the field.