In response to the low precision of ore image segmentation under complex working conditions, an improved Mask R-CNN segmentation algorithm is proposed. The traditional Mask R-CNN uses a simple deconvolution operation to generate masks, which can lead...
This paper presents an enhanced approach to kidney segmentation using a modified CLAHE preprocessing method, aimed at improving image clarity and CNN performance on the KiTS19 dataset. To assess the impact of the modified CLAHE method, we conducted q...
Although the Mask region-based convolutional neural network (R-CNN) model possessed a dominant position for complex and variable road scene segmentation, some problems still existed, including insufficient feature expressive ability and low segmentat...
Pavement surface defects such as potholes pose significant safety risks and accelerate infrastructure deterioration. Accurate and automated detection of such defects requires both advanced sensing technologies and robust deep learning models. In this...
Single image depth estimation works fail to separate foreground elements because they can easily be confounded with the background. To alleviate this problem, we propose the use of a semantic segmentation procedure that adds information to a depth es...
Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development...
Protein crystallization is the bottleneck in macromolecular crystallography, and crystal recognition is a very important step in the experiment. To improve the recognition accuracy by image classification algorithms further, the Mask R-CNN model is i...
With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segment...
Semantic segmentation of remote sensing images has been widely used in environmental protection, geological disaster discovery, and natural resource assessment. With the rapid development of deep learning, convolutional neural networks (CNNs) have do...
Mitochondria are the organelles that generate energy for the cells. Many studies have suggested that mitochondrial dysfunction or impairment may be related to cancer and other neurodegenerative disorders such as Alzheimer’s and Parkinson’...
Medical image segmentation techniques are vital to medical image processing and analysis. Considering the significant clinical applications of brain tumour image segmentation, it represents a focal point of medical image segmentation research. Most o...
In the context of Industry 4.0, the most popular way to identify and track objects is to add tags, and currently most companies still use cheap quick response (QR) tags, which can be positioned by computer vision (CV) technology. In CV, instance segm...
Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in so...
Semantic segmentation by using remote sensing images is an efficient method for agricultural crop classification. Recent solutions in crop segmentation are mainly deep-learning-based methods, including two mainstream architectures: Convolutional Neur...
Rice yield is closely related to the number and proportional area of rice panicles. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective. To solve this problem, we propose an improved Mask R-CNN...
Stones are one of the primary objects that impede the normal activity of underground pipelines. As human intervention is difficult inside a narrow underground pipe, a robot with a machine vision system is required. In order to remove the stones durin...
Owing to the variable shapes, large size difference, uneven grayscale, and dense distribution among biological cells in an image, it is very difficult to accurately detect and segment cells. Especially, it is a serious challenge for some microscope i...
Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases....
Object-detection methods based on deep learning play an important role in achieving machine automation. In order to achieve fast and accurate autonomous detection of stacked electronic components, an instance segmentation method based on an improved...
Accurately segmenting grape clusters and detecting grape varieties in orchards is beneficial for orchard staff to accurately understand the distribution, yield, growth information, and efficient mechanical harvesting of different grapes. However, fac...
Pancreas segmentation in computed tomography (CT) images remains a challenging task due to the organ’s variable shape, size, and low contrast against surrounding tissues. In this study, we propose a two-stage pancreas segmentation framework tha...
Image segmentation is an important method in the field of image processing, while infrared (IR) image segmentation is one of the challenges in this field due to the unique characteristics of IR data. Infrared imaging utilizes the infrared radiation e...
Gastrointestinal endoscopy is widely conducted for the early detection of gastric cancer. However, it is often difficult to detect early gastric cancer lesions and accurately evaluate the invasive regions. Our study aimed to develop a detection and s...
To prevent or deal with chronic diseases, using a smart device, automatically classifying food categories, estimating food volume and nutrients, and recording dietary intake are considered challenges. In this work, a novel real-time vision-based meth...
Mapping new and old buildings are of great significance for understanding socio-economic development in rural areas. In recent years, deep neural networks have achieved remarkable building segmentation results in high-resolution remote sensing images...
Medical image segmentation still faces three critical challenges: insufficient joint modeling of local details and long-range dependencies, the high computational burden of transformer-based architectures for high-resolution inputs, and performance d...
The construction and inspection of reinforcement rebar currently rely entirely on manual work, which leads to problems such as high labor requirements and labor costs. Rebar image detection using deep learning algorithms can be employed in constructi...
Accurate defect instance segmentation is essential for automated quality inspection of piezoelectric crystals, where scratches, stains, and other surface defects often show large scale variation, irregular morphology, and weak contrast against comple...
As a highly productive rice, ratoon rice is widely planted worldwide, but the rolling of rice stubble in mechanical harvesting severely limits its total yield; based on this, some scholars have proposed rolled rice stubble righting machines. However,...
It is important for food recognition to separate each ingredient within a food image at the pixel level. Most existing research has trained a segmentation network on datasets with pixel-level annotations to achieve food ingredient segmentation. Howev...
Windows, as key components of building facades, have received increasing attention in facade parsing. Convolutional neural networks have shown promising results in window extraction. Most existing methods segment a facade into semantic categories and...
The precise application of tightening torque is one of the important measures to ensure accurate bolt connection and improvement in product assembly quality. Currently, due to the limited assembly space and efficiency, a wrench without the function o...
In recent years, UNet and its improved variants have become the main methods for medical image segmentation. Although these models have achieved excellent results in segmentation accuracy, their large number of network parameters and high computation...
The segmentation of Hehuang shadow puppet images holds significant value for artistic creation and cultural preservation. However, it still faces challenges such as the subjective nature of manual segmentation and inaccurate boundary delineation in c...
Background: A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small si...
Infrared images of caged chickens can provide valuable insights into their health status. Accurately detecting and segmenting individual chickens in these images is essential for effective health monitoring in large-scale chicken farming. However, th...
Timely and accurate acquisition of crop type information is significant for irrigation scheduling, yield estimation, harvesting arrangement, etc. The unmanned aerial vehicle (UAV) has emerged as an effective way to obtain high resolution remote sensi...
Since the mature green tomatoes have color similar to branches and leaves, some are shaded by branches and leaves, and overlapped by other tomatoes, the accurate detection and location of these tomatoes is rather difficult. This paper proposes to use...
High-resolution UAV imagery paired with a convolutional neural network approach offers significant advantages in accurately measuring forestry ecosystems. Despite numerous studies existing for individual tree crown delineation, species classification...
Coastal erosion due to extreme events can cause significant damage to coastal communities and deplete beaches. Post-storm beach recovery is a crucial natural process that rebuilds coastal morphology and reintroduces eroded sediment to the subaerial b...
Hand gesture-based sign language recognition is a prosperous application of human– computer interaction (HCI), where the deaf community, hard of hearing, and deaf family members communicate with the help of a computer device. To help the deaf c...
Rubber trees in southern China are often impacted by natural disturbances that can result in a tilted tree body. Accurate crown segmentation for individual rubber trees from scanned point clouds is an essential prerequisite for accurate tree paramete...
During flood disasters, on-site operations often face the “triple disruption” of network outages, power cuts and blocked roads. This renders terrestrial cellular infrastructure inoperable and disrupts communication links. Satellite intern...
Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured...
Camellia oleifera is a crop of high economic value, yet it is particularly susceptible to various diseases and pests that significantly reduce its yield and quality. Consequently, the precise segmentation and classification of diseased Camellia leave...
Background: Accurate detection of intracranial aneurysms, especially those smaller than 3 mm, remains a critical challenge in neurovascular imaging due to their subtle morphology and low contrast in Time-of-Flight Magnetic Resonance Angiography (TOF-...
Image segmentation is one of the important applications of deep learning models, such as U-Net and Mask R-CNN, in medical imaging. The image segmentation process enables automated extraction of important information within images, including spine X-r...
Traditional and convolutional neural network (CNN)-based geographic object-based image analysis (GeOBIA) land-cover classification methods prosper in remote sensing and generate numerous distinguished achievements. However, a bottleneck emerges and h...
The high-precision instance segmentation of tree saplings is a fundamental prerequisite for the high-throughput phenotypic analysis of individual seedlings in intelligent tree breeding and precision silviculture. However, sapling segmentation remains...
Building segmentation is crucial for applications extending from map production to urban planning. Nowadays, it is still a challenge due to CNNs’ inability to model global context and Transformers’ high memory need. In this study, 10 CNN...
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