LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation

Rahaman

et al. 2021

Artif Intell Rev

Self Cite

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

Section: Discussionmentioning

confidence: 99%

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Rahaman

et al. 2021

Artif Intell Rev

Self Cite

“…Recently, neural networks have been introduced to various tasks of computer vision including image segmentation, image classification, and image SR [39][40][41]. Compared with traditional image SR methods (e.g., sparse reconstruction), neural network SR methods can learn an end-to-end mapping between high-resolution (HR) and low-resolution (LR) images, and complex features can be automatically learned by hidden layers.…”

Section: Sr Methods Based On Neural Networkmentioning

confidence: 99%

Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks

2022

Sensors

High-resolution digital elevation models (DEMs) play a critical role in geospatial databases, which can be applied to many terrain-related studies such as facility siting, hydrological analysis, and urban design. However, due to the limitation of precision of equipment, there are big gaps to collect high-resolution DEM data. A practical idea is to recover high-resolution DEMs from easily obtained low-resolution DEMs, and this process is termed DEM super-resolution (SR). However, traditional DEM SR methods (e.g., bicubic interpolation) tend to over-smooth high-frequency regions on account of the operation of averaging local variations. With the recent development of machine learning, image SR methods have made great progress. Nevertheless, due to the complexity of terrain characters (e.g., peak and valley) and the huge difference between elevation field and image RGB (Red, Green, and Blue) value field, there are few works that apply image SR methods to the task of DEM SR. Therefore, this paper investigates the question of whether the state-of-the-art image SR methods are appropriate for DEM SR. More specifically, the traditional interpolation method and three excellent SR methods based on neural networks are chosen for comparison. Experimental results suggest that SRGAN (Super-Resolution with Generative Adversarial Network) presents the best performance on accuracy evaluation over a series of DEM SR experiments.

“…In light of the characteristics of defect detection tasks, the 3 × 3 depth-wise convolutional layers in the right branch of both basic unit ( Figure 2 a) and spatial down-sampling (2×) unit ( Figure 2 b) are replaced by 2 consecutive 1-bit 3 × 3 convolutions while the left branch of down-sampling unit ( Figure 2 b) is substituted by 1-bit 5 × 5 convolutions, in order to enjoy a larger receptive field to detect defects of various scales. Applying multiple 3 × 3 convolutions in sequence to enjoy a larger reception field is a common and efficient idea in object segmentation [ 38 , 39 , 40 ] to save parameters in the meantime. Correspondingly, the kernel size of the first convolution layer is defined to be 9 × 9 as well.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Binary Neural Network for Automated Visual Surface Defect Detection

Liu

et al. 2021

Sensors

As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process. To address these issues, this paper introduces binary networks into the area of surface defect detection for the first time, for the reason that binary networks prohibitively constrain weight and activation to +1 and −1. The proposed Bi-ShuffleNet and U-BiNet utilize binary convolution layers and activations in low bitwidth, in order to reach comparable performances while incurring much less computational cost. Extensive experiments are conducted on real-life NEU and Magnetic Tile datasets, revealing the least OPs required and little accuracy decline. When classifying the defects, Bi-ShuffleNet yields comparable results to counterpart networks, with at least 2× inference complexity reduction. Defect segmentation results indicate similar observations. Some network design rules in defect detection and binary networks are also summarized in this paper.