Intelligent Image and Video Analytics

During the last years, Machine Learning approaches gathered more and more interest in the field of image processing. Deep Convolutional Neural Networks (CNN) are the state-of-the-art approach for classification and detection of image content. The breakthrough of CNNs was remarked by the recognition of handwritten numbers and later by the recognition of faces or licence plates.

Nowadays powerful and real time applicable networks are used to detect multiple different classes in images. By that, persons, vehicles, obstacles or traffic signs can be detected in real time. The performance of a CNN is rated by its detection rate. Ideally, improvements increase the speed and accuracy needed for correctly identifying the image content.

Furthermore, in recent years the topic of environmental and climate protection has increasingly become the focus of politics and the population. For example, early forest fire detection systems can drastically reduce the time to detection and provide early notice of the danger to residents. AI systems are also being used in the food industry to distinguish spoiled food from food that is still edible, with the goal of ensuring that less ends up in the trash. In the recycling sector, this new technology can also be used to reliably and efficiently detect plastics, thus enabling them to be reused.

Contact
Katja Kossira, M.Sc.
E-Mail: katja.kossira@fau.de
Link to person
PD Dr.-Ing. habil. Jürgen Seiler
E-Mail: juergen.seiler@fau.de
Link to person

Application of Methods of Intelligent Image and Video Analysis in the Field of Recycling

The increasing relevance of the topic of environmental and climate protection affects many areas of our lives: Beaches on vacation are littered with disposable plastic bottles and there is a huge garbage swirl floating in the ocean. Temperatures are rising, yet you can hardly find anything in the supermarket that isn’t wrapped in plastic. Currently, 67% of this waste is thermally recycled, i.e. incinerated, but the efficiency of this process is merely 30%, while a lot of CO2 is emitted in the process (Source: Plastikatlas 20191). Since recycling is significantly more efficient at 85% and also emits less CO2, it is important to create efficient and inexpensive methods to classify plastics properly so that they can be reused.

To support this development, a new approach will be explored that uses intelligent image and video analysis with the aid of AI, aiming to decide which type of plastic the sample belongs to. The idea origins in the discovery of carbon chains of plastics reflecting light at different wavelengths in the infrared range to different extents, resulting in various colors for characteristic plastics. This is shown exemplary in the following figure (left: original representation, right: superimposition of images taken with filters of wavelengths 850nm, 1000nm and 1150nm).

 

In order to automatically recognize plastics in the future, the camera array for multispectral imaging (CAMSI) will be further developed and adapted to the requirements. The spectral fingerprints of the different plastic classes will be determined and subsequently used to train a model. In addition, a decision matrix is created that determines the optimal filter selection based on significant peaks of the spectra. The output is then used for an unambiguous classification and the recyclable material can be fed into the recycling process. The new method is to be used, for example, in deposit machines or at sorting facilities of recycling centers.

 

1 https://www.bund.net/fileadmin/user_upload_bund/publikationen/chemie/chemie_plastikatlas_2019.pdf

Embedded Perception for Autonomous Vehicles

Contact
Maximiliane Gruber, M. Sc.
E-Mail: maximiliane.gruber@fau.de
Link to person
Kamal Nambiar, M. Sc.
E-Mail: kamal.nambiar@fau.de
Link to person
PD Dr.-Ing. habil. Jürgen Seiler
E-Mail: juergen.seiler@fau.de
Link to person

The primary goal of Embedded Perception is to develop a data-driven framework that enables object recognition in road data, thereby facilitating the advancement of autonomous driving vehicles. The hardware system comprises multiple cameras and LiDAR sensors, while the software system consists of both an offline and an online perception component. The online component is designed to deliver real-time and precise results, effectively utilizing the limited computing resources onboard. On the other hand, the offline component operates independently on the same data once the recordings are complete, benefiting from more extensive compute resources and enabling the execution of highly complex algorithms. As a result, the outcomes are expected to be more dependable. Such a system can be utilized to generate extensive reference datasets encompassing diverse traffic and weather conditions.

The development of the Embedded Perception Framework poses several signal processing challenges, including sensor calibration, data compression, multi-modal data analysis and fusion, data augmentation, and the development of perception algorithms, among others. The key areas of our research can be broadly divided into:

  • LiDAR-Camera Self-Calibration  To address the requirements of a diverse operating domain and avoid heuristically tuned algorithms, we propose a self-calibration pipeline based on deep learning. This pipeline aims to perform simultaneous intrinsic calibration of the camera sensor and extrinsic calibration of the LiDAR camera setup.
  • LiDAR Point Cloud Compression  Efficient compression algorithms are crucial for handling the substantial data produced by LiDAR sensors, during storage and transmission. Apart from reducing memory and storage costs, smaller file sizes facilitate faster information exchange between autonomous vehicles, thereby decreasing the overall latency of the perception system. Given that these signals primarily serve downstream perception tasks like object detection, our research goes beyond traditional quality metrics like PSNR, and we develop compression algorithms that also consider their impact on detection accuracy in state-of-the-art perception algorithms.

 

Fig. 1: Demonstrator setup consisting of stereo camera and LiDAR sensor.
Fig. 2: Performance improvement by our methods for learning image distortions in JPEG2000 coded images.
  • Learning Image Distortions for Domain Adaptation Since data-driven methods learn a particular task from annotated data samples, such methods heavily depend on the quality of the training data. That is, the training data must sufficiently resemble the data encountered during application and testing. In practice, however, unknown distortion can lead to a domain gap between training and test data, impeding the performance of a machine vision system. Examples of such unknown distortions that occur in image data are blur, noise, and coding. These distortions differ for example for different camera systems. Fig. 2a illustrates an example of the drop in performance for JPEG2000-encoded test data if the training data did not contain such distortions. To address the performance drop of image processing systems in such cases, we explore methods for learning the above mentioned unknown image distortions. The learned image distortions are then emulated on clean data, which serve as additional training data to improve the performance of image processing systems. In our work, we consider machine vision systems as a black box and develop general methods that can be used for various tasks. This approach is also called pixel-level domain adaptation. Fig. 2b shows the improvement in object detection, when using our method.
  • Road Lane Detection  Road Lane detection is a critical component for ensuring the safe navigation of autonomous vehicles. Our research focuses on developing a neural network-based model that leverages fused features from LiDAR and monocular camera signals to generate a 3D profile of the road. The lane detection capabilities of our model exhibit robustness to road slopes and provide accurate depth estimation.
  • Object Detection and Object Tracking  Object detection involves identifying and localizing road users and road signs within a captured frame from the vehicle’s sensors. Object tracking enhances perception by associating identical objects across frames, enabling motion estimation for dynamic entities like pedestrians and vehicles. In the task of 3D multi-object tracking, two approaches are commonly used. The first approach employs an end-to-end trainable object tracking system for a sequence of frames. The second approach involves using a 3D object detector for each frame individually, followed by association using a Kalman filter and the Hungarian algorithm. Our research investigates both approaches and further optimizes them to address the challenges of speed and accuracy in our online and offline processing pipelines.

Contact
PD Dr.-Ing. habil. Jürgen Seiler
E-Mail: juergen.seiler@fau.de
Link to Person

Artificial intelligence for quality assurance in industrial manufacturing

The growing amount of automation influences every aspect of our daily live. A topic that is very strongly affected by this transformation is industrial manufacturing. Modern production lines already run nearly without any human action. Due to the upcoming changes that come with Industry 4.0 this phenomenon will be even more reinforced. In order to guarantee the quality of the items produced in that manner, a final visual quality control has to be conducted. Up to now, this is still often done manually. As this is a tiring, monotonous and exhausting labour, automated solutions are searched for this problem as well.

Since some years, there are several automated optical inspection systems. Up to now, those systems use classical image processing techniques. Those techniques require a strict calibration and adjustment of the tolerance ranges and test criteria for the current item before the inspection can start. Especially in the context of Industry 4.0 with often changing inspection tasks and small batch series this is becoming very inefficient. As an alternative, a new approach is developed that uses Machine Learning techniques to assess whether an item is erroneous or not. The traditional approach to solve this problem is to acquire a huge amount to images of erroneous and fault-less items in order to use these images for the training of a neural network. The problem with this approach is that first of all a sufficiently large number of items has to be produced to acquire images of them. Another restriction is that enough erroneous items have to be available to guarantee a reliable training.

 

 

Especially in terms of a fast adaption of the production and the fabrication of individual items, this approach is not suited well. In order to eliminate the production and following assessment of items for the generation of training data for the neural network another approach is followed here. Here the training data is not acquired by a camera, but generated by a model. Therefore, images of the item that will be produced are generated based on a model of this item. The neural network is trained on these model-generated images and afterwards applied to the real acquired images of the item that is to be inspected. By this a separation into good and erroneous items can be performed.

 

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