In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.

Detection of grapes in real-life images is a serious task solved by researchers dealing with precision viticulture. In the case of white wine varieties, grape detectors based on SVMs classifiers, in combination with a HOG descriptor, have proven to be very efficient. Simplified versions of the detectors seem to be the best solution for practical applications. They offer the best known performance versus time-complexity ratio. As our research showed, a conversion of RGB images to grayscale format, which is implemented at an image preprocessing level, is ideal means for further improvement of performance of the detectors. In order to enhance the ratio, we explored relevance of the conversion in a context of a detector potential sensitivity to a rotation of berries. For this purpose, we proposed a modification of the conversion, and we designed an appropriate method for a tuning of such modified detectors. To evaluate the effect of the new parameter space on their performance, we developed a specialized visualization method. In order to provide accurate results, we formed new datasets for both tuning and evaluation of the detectors. Our effort resulted in a robust grape detector which is less sensitive to image distortion.

The current demand for remote work, remote teaching and video conferencing has brought a surge not only in network traffic, but unfortunately, in the number of attacks as well. Having reliable, safe and secure functionality of various network services has never been more important. Another serious phenomenon that is apparent these days and that must not be discounted is the growing use of artificial intelligence techniques for carrying out network attacks. To combat these attacks, effective protection methods must also utilize artificial intelligence. Hence, we are introducing a specific neural network-based decision procedure that can be considered for application in any flow characteristic-based network-traffic-handling controller. This decision procedure is based on a convolutional neural network that processes the incoming flow characteristics and provides a decision; the procedure can be understood as a firewall rule. The main advantage of this decision procedure is its depiction process, which has the ability to transform the incoming flow characteristics into a graphical structure. Graphical structures are regarded as very efficient data structures for processing by convolutional neural networks. This article’s main contribution consists of the development and improvement of the depiction process using a genetic algorithm. The results presented at the end of the article show that the decision procedure using an optimized depiction process brings significant improvements in comparison to previous experiments.

Robotic manipulation with a nontrivial object providing various types of grasping points is of an industrial interest. Here, an efficient method of simultaneous detection of the grasping points is proposed. Specifically, two different 3 degree-of-freedom end effectors are considered for simultaneous grasping. The method utilizes an RGB data-driven perception system based on a specifically designed fully convolutional neural network called attention squeeze parallel U-Net (ASP U-Net). ASP U-Net detects grasping points based on a single RGB image. This image is transformed into a schematic grayscale frame, where the positions and poses of the grasping points are coded into gradient geometric shapes. In order to approve the ASP U-Net architecture, its performance was compared with nine competitive architectures using metrics based on generalized intersection over union and mean absolute error. The results indicate its outstanding accuracy and response time. ASP U-Net is also computationally efficient enough. With a more than acceptable memory size (77 MB), the architecture can be implemented using custom single-board computers. Here, its capabilities were tested and evaluated on the NVIDIA Jetson NANO platform.

The recognition of wine grapes in images acquired in natural environment is a serious issue solved by researches dealing with precision viticulture. The detection of wine grapes of red kinds is a well managed problem. On the other hand, the detection of white grapes is still a challenging task. In this contribution, the classifier for white wine grapes recognition is introduced and evaluated. The classifier is based on an artificial neural network and is used in two ways which differ in image representation. Namely, the pixel intensities and histogram of oriented gradients are used for the representation of images. Then, feedforward multilayer neural network is applied as a classifier. The classifiers based on the histograms of oriented gradients seemed to be very effective - they were almost error free from the cross validation point of view and they performed well with the independent testing data sets, too. On the other hand, the representation using pixel intensities was stated as insufficient for classification using our approach.