Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review

Development of an Artificial Vision for a Parallel Manipulator Using Machine-to-Machine Technologies

This research focuses on developing an artificial vision system for a flexible delta robot manipulator and integrating it with machine-to-machine (M2M) communication to optimize real-time device interaction. This integration aims to increase the speed of the robotic system and improve its overall performance. The proposed combination of an artificial vision system with M2M communication can detect and recognize targets with high accuracy in real time within the limited space considered for positioning, further localization, and carrying out manufacturing processes such as assembly or sorting of parts. In this study, RGB images are used as input data for the MASK-R-CNN algorithm, and the results are processed according to the features of the delta robot arm prototype. The data obtained from MASK-R-CNN are adapted for use in the delta robot control system, considering its unique characteristics and positioning requirements. M2M technology enables the robot arm to react quickly to changes, such as moving objects or changes in their position, which is crucial for sorting and packing tasks. The system was tested under near real-world conditions to evaluate its performance and reliability.

Remote intelligent perception system for multi-object detection

Introduction

During the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.

Methods

Advances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.

Results

The performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.

Discussion

Furthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

Self-powered semitransparent WS2/LaVO3vertical-heterostructure photodetectors by employing interfacial hexagonal boron nitride

Two-dimensional (2D) semiconductor and LaVO3materials with high absorption coefficients in the visible light region are attractive structures for high-performance photodetector (PD) applications. Insulating 2D hexagonal boron nitride (h-BN) with a large band gap and excellent transmittance is a very attractive material as an interface between 2D/semiconductor heterostructures. We first introduce WS2/h-BN/LaVO3semitransparent PD. The photo-current/dark current ratio of the device exhibits a delta-function characteristic of 4 × 105at 0 V, meaning 'self-powered'. The WS2/h-BN/LaVO3PD shows up to 0.27 A W-1responsivity (R) and 4.6 × 1010cm Hz1/2W-1detectivity (D*) at 730 nm. Especially, it was confirmed that theD* performance improved by about 5 times compared to the WS2/LaVO3device at zero bias. Additionally, it is suggested that the PD maintains 87% of its initialRfor 2000 h under the atmosphere with a temperature of 25 °C and humidity of 30%. Based on the above results, we suggest that the WS2/h-BN/LaVO3heterojunction is promising as a self-powered optoelectronic device.