Detecting Out-of-distribution Objects Using Neuron Activation Patterns

Object detection is essential to many perception algorithms used in modern robotics applications. Unfortunately, the existing models share a tendency to assign high confidence scores for out-of-distribution (OOD) samples. Although OOD detection has been extensively studied in recent years by the computer vision (CV) community, most proposed solutions apply only to the image recognition task. Real-world applications such as perception in autonomous vehicles struggle with far more complex challenges than classification. In our work, we focus on the prevalent field of object detection, introducing Neuron Activation PaTteRns for out-of-distribution samples detection in Object detectioN (NAPTRON). Performed experiments show that our approach outperforms state-of-the-art methods, without the need to affect in-distribution (ID) performance. By evaluating the methods in two distinct OOD scenarios and three types of object detectors we have created the largest open-source benchmark for OOD object detection.

Combating noisy labels in object detection datasets

The quality of training datasets for deep neural networks is a key factor contributing to the accuracy of resulting models. This is even more important in difficult tasks such as object detection. Dealing with errors in these datasets was in the past limited to accepting that some fraction of examples is incorrect or predicting their confidence and assigning appropriate weights during training. In this work, we propose a different approach. For the first time, we extended the confident learning algorithm to the object detection task. By focusing on finding incorrect labels in the original training datasets, we can eliminate erroneous examples in their root. Suspicious bounding boxes can be re-annotated in order to improve the quality of the dataset itself, thus leading to better models without complicating their already complex architectures. We can effectively point out 99\% of artificially disturbed bounding boxes with FPR below 0.3. We see this method as a promising path to correcting well-known object detection datasets.

The utilization of spherical camera in simulation for service robotics

Safety is one of the most critical factors in robotics, especially when robots have to collaborate with people in a~shared environment. Testing the physical systems, however, must focus on much more than just software. One of the common steps in robotic system development is the utilization of simulators, which are very good for tasks like navigation or manipulation. Testing vision systems is more challenging, as the simulated data often is far from the real camera readings. In this paper, we show the advantages of using the spherical camera for recording the sequences of test images and a~way to integrate those with existing robotic simulator. The presented system also has the possibility to be extended with rendered objects to further improve its usability.