Fast Object Detection Using Dimensional Based Features for Public Street Environments

Abstract

Modern object recognition algorithms have very high precision. At the same time, they require high computational power. Thus, widely used low-power IoT devices, which gather a substantial amount of data, cannot directly apply the corresponding machine learning algorithms to process it due to the lack of local computational resources. A method for fast detection and classification of moving objects for low-power single-board computers is shown in this paper. The developed algorithm uses geometric parameters of an object as well as scene-related parameters as features for classification. The extraction and classification of these features is a relatively simple process which can be executed by low-power IoT devices. The algorithm aims to recognize the most common objects in the street environment, e.g., pedestrians, cyclists, and cars. The algorithm can be applied in the dark environment by processing images from a near-infrared camera. The method has been tested on both synthetic virtual scenes and real-world data. The research showed that a low-performance computing system, such as a Raspberry Pi 3, is able to classify objects with acceptable frame rate and accuracy.

Wavelet thresholding and F-NLM filtering based denoising algorithms applied to high resolution SAR ship detection

“…Where: 𝑇 𝑃 represents the number of ships correctly identified, 𝐹 𝑃 represents the number of non-ships identified as ships and 𝐹 𝑁 represents the number of ships identified as non-ships. The formula for calculating the AP is shown in (13). (13) A default threshold of 0.5 for AP50 was chosen as an indicator of the detection performance in this paper because the higher the AP value, the better the detection performance.…”

“…The application of SAR ship data for the detection of ship targets on the water will be able to effectively enhance the early warning capability of sea defense, strengthen the detection and management of fisheries resources, as well as possess an extensive range of application prospects along with vital significance for national development 2,3 . At the current stage, there are four predominant methods for target detection based on SAR images: Target detection methods based on structural features [4][5][6] , grey-scale features [7][8][9] , texture features [10][11][12][13] , and deep learning [14][15][16][17] . In comparison, deep learning-based methods boast powerful feature extraction capabilities and are capable of automatically learning structured features to successfully achieve high-precision recognition of detection targets [18][19][20] .…”

Smart and Sentient Retail High Streets

“…The data science and machine learning capabilities of Edge AI can now be broadcast in hyper-local range from edge devices. For example, Matveev et al [210] have shown that rather sophisticated deep learning for object and feature detection on streetscape scenes can be performed on edge devices (with sufficient resolution to identify height and width data), with results communicated over IoT networks. Wi-Edge may also provide support for mobile forms of edge computing: so-termed "Mobile Edge Computing" (MEC) [211].…”

A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces

“…In this work, we propose the use of the Motion Check method followed by the blob-based counting algorithm to build a simple, computationally fast, and yet very versatile solution which is able to count pieces, mostly based on color and morphology. We avoided testing very advanced MV solutions, based on Convolutional Neural Networks (CNN), for the counting algorithm, as they are too computationally intensive [ 28 , 29 ] and, given the mandatory necessity of our system to work in real-time, they likely require huge computational resources such as dedicated GPUs. Furthermore, the usage of a CNN is probably excessive in our context, as the environment is very restricted, and only hands and products interfere in the framed area.…”