In this paper, we present our investigation of the 2D Hand Gesture Recognition (HGR) which may be suitable for the control of the Automated Guided Vehicle (AGV). In real conditions, we deal with, among others, a complex background, changing lighting conditions, and different distances of the operator from the AGV. For this reason, in the article, we describe the database of 2D images created during the research. We tested classic algorithms and modified them by us ResNet50 and MobileNetV2 which were retrained partially using the transfer learning approach, as well as proposed a simple and effective Convolutional Neural Network (CNN). As part of our work, we used a closed engineering environment for rapid prototyping of vision algorithms, i.e., Adaptive Vision Studio (AVS), currently Zebra Aurora Vision, as well as an open Python programming environment. In addition, we shortly discuss the results of preliminary work on 3D HGR, which seems to be very promising for future work. The results show that, in our case, from the point of view of implementing the gesture recognition methods in AGVs, better results may be expected for RGB images than grayscale ones. Also using 3D imaging and a depth map may give better results.

This paper presents a method for human detection using a laser scanner with vision or infrared images. Mobile applications require reliable and efficient methods for human detection, especially as a part of driver assistance systems, including pedestrian collision systems. The authors propose an efficient method for multimodal human detection based on a combination of the features and context information. Strictly, the human is detected in the vision/infrared images using a combination of local binary patterns and histogram of oriented gradients features with a neural network in a cascade manner. Next, using coordinates of detected humans from the vision system, the moving trajectory is predicted until the scanner working distance is reached by the individual human. Then the segmentation of data from the laser scanner is further carried out with respect to the predicted trajectory. Finally, human detection in the laser scanner working distance is performed based on modelling of the human legs. The modelling is based on the adaptive breakpoint detection algorithm and proposed improved polylines definition and fitting algorithm. The authors conducted a set of experiments in predefined scenarios, discussed the identified weakness and advantages of the proposed method, and outlined detailed future work, especially for night-time and low-light conditions.

The moisture of bulk material has a significant impact on the energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. This research aims to develop computer vision and thermovision techniques for the on-site estimation of moisture content in copper ore, for use, e.g., in dry grinding installations. The influence of particle size on the results of moisture estimation is also studied. The tested granular material was copper ore of particle size 0–2 mm and relative moisture content of 0.5–11%. Both vision and thermovision images were taken at standard and macro scales. The results suggest that median-intensity vision images monotonically reflect copper ore moisture in the range of about 0.5–5%. Suitable models were identified and cross-validated here. In contrary, thermograms should not be analyzed simply for their mean temperature but treated with computer vision processing algorithms.

IEEE 1451.4 standard introduced the full “plug and play” for sensors and actuators thanks to the Transducer Electronic Datasheet (TEDS). The only requirement for the measuring system is compatibility with 1451.4. The advantage of this standard among all series of IEEE 1451 is that the Network Capable Application Processor (NCAP) is not necessary. This caused the IEEE 1451.4 to become the most popular and widely available on the market. This paper presents the implementation of the IEEE 1451.4 standard in custom made vibration transducers, based on MEMS accelerometers. The transducers were calibrated and the results of calibration are stored in TEDS added to the sensor. Then the transducers with TEDS were utilized in an experimental machine conditioning system based on embedded RT/FPGA platform.