Remote intelligent perception system for multi-object detection

Abstract

IntroductionDuring 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.MethodsAdvances 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.ResultsThe 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.DiscussionFurthermore, 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.

A numerical aproach to dispersion-dissipation-reaction model: third order KdV-Burger-Fisher equation

“…Some nonlinear models are investigated such as fuzzy neural network [29,30], optimal deep learning [31], optical solutions [32,33]. Some features have been used to nonlinear model a wide range of physical systems, including multi-objects detection [34], finger photoplethysmography [35], asymptotic analysis [36], fourth-order time-fractional PDE [37], eigenvalue problem [38]. There are numerous reliable and well-developed methods for obtaining the exact solutions including, rate-distortion optimized quantization [39], optical system for suppression of laser echo energy [40], multispectral subsurface reflectance [41], bistable origami flexible gripper [42]., Scholars have conducted an in-depth analysis on framework for color attributes [43,44].…”