Semantic versus instance segmentation in microscopic algae detection

Ruíz-Santaquiteria, Jesús; Bueno, Gloria; Deniz, Oscar; Vállez, Noelia; Cristóbal, Gabriel

doi:10.1016/j.engappai.2019.103271

Cited by 87 publications

(57 citation statements)

References 25 publications

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“…The identification of genera in water samples is of central importance in assessing water quality in vision. Over the years, this procedure has mainly relied on manual counting [1], which inevitably suffers limitations in consuming time, manpower, and energy. Thus, it is urgent to develop vision sensing-based automatic tools capable of expediting the detection and quantification process.…”

Section: Introductionmentioning

confidence: 99%

Weighted Mask R‐CNN for Improving Adjacent Boundary Segmentation

Suh

Park

et al. 2021

Journal of Sensors

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In the recent era of AI, instance segmentation has significantly advanced boundary and object detection especially in diverse fields (e.g., biological and environmental research). Despite its progress, edge detection amid adjacent objects (e.g., organism cells) still remains intractable. This is because homogeneous and heterogeneous objects are prone to being mingled in a single image. To cope with this challenge, we propose the weighted Mask R-CNN designed to effectively separate overlapped objects in virtue of extra weights to adjacent boundaries. For numerical study, a range of experiments are performed with applications to simulated data and real data (e.g., Microcystis, one of the most common algae genera and cell membrane images). It is noticeable that the weighted Mask R-CNN outperforms the standard Mask R-CNN, given that the analytic experiments show on average 92.5% of precision and 96.4% of recall in algae data and 94.5% of precision and 98.6% of recall in cell membrane data. Consequently, we found that a majority of sample boundaries in real and simulated data are precisely segmented in the midst of object mixtures.

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Section: Introductionmentioning

confidence: 99%

Weighted Mask R‐CNN for Improving Adjacent Boundary Segmentation

Suh

Park

et al. 2021

Journal of Sensors

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“…The limitation of the segmentation-first strategy is that objects receive the same semantic information for all instances. In contrast, the Mask-RCNN makes a clear distinction between objects and gives per-object information, showing promising results even when objects overlap [72]. Therefore, the instance segmentation in the remote sensing data predominantly uses the Mask-RCNN/Faster-RCNN architecture [129][130][131][132].…”

Section: Discussionmentioning

confidence: 99%

“…Instance segmentation has applications in several areas of knowledge: medicine [69,70], biology [71,72], livestock [73,74], agronomy [75,76], among others. However, remote sensing application is still restricted, highlighting its use in the automatic detection of the following targets: marine oil spill [77], building [78,79], vehicle [80], and ship [81].…”

Section: Introductionmentioning

confidence: 99%

Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach

et al. 2020

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Instance segmentation is the state-of-the-art in object detection, and there are numerous applications in remote sensing data where these algorithms can produce significant results. Nevertheless, one of the main problems is that most algorithms use Red, Green, and Blue (RGB) images, whereas Satellite images often present more channels that can be crucial to improve performance. Therefore, the present work brings three contributions: (a) conversion system from ground truth polygon data into the Creating Common Object in Context (COCO) annotation format; (b) Detectron2 software source code adaptation and application on multi-channel imagery; and (c) large scene image mosaicking. We applied the procedure in a Center Pivot Irrigation System (CPIS) dataset with ground truth produced by the Brazilian National Water Agency (ANA) and Landsat-8 Operational Land Imager (OLI) imagery (7 channels with 30-m resolution). Center pivots are a modern irrigation system technique with massive growth potential in Brazil and other world areas. The round shapes with different textures, colors, and spectral behaviors make it appropriate to use Deep Learning instance segmentation. We trained the model using 512 × 512-pixel sized patches using seven different backbone structures (ResNet50- Feature Pyramid Network (FPN), Resnet50-DC5, ResNet50-C4, Resnet101-FPN, Resnet101-DC5, ResNet101-FPN, and ResNeXt101-FPN). The model evaluation used standard COCO metrics (Average Precision (AP), AP50, AP75, APsmall, APmedium, and AR100). ResNeXt101-FPN had the best results, with a 3% advantage over the second-best model (ResNet101-FPN). We also compared the ResNeXt101-FPN model in the seven-channel and RGB imagery, where the multi-channel model had a 3% advantage, demonstrating great improvement using a larger number of channels. This research is also the first with a mosaicking algorithm using instance segmentation models, where we tested in a 1536 × 1536-pixel image using a non-max suppression sorted by area method. The proposed methodology is innovative and suitable for many other remote sensing problems and medical imagery that often present more channels.

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“…13 Instance segmentation uses object detection method to frame different instances in the image and makes pixel by pixel prediction in different instance regions. 14 Using the mask as the label of pixel prediction, the mask is a binary image composed of 0 and 1. Generally, the image mask is defined by the region of interest.…”

Section: Related Workmentioning

confidence: 99%

Research on abnormal object detection in specific region based on Mask R-CNN

Xiong

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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As an information carrier with rich semantics, image plays an increasingly important role in real-time monitoring of logistics management. Abnormal objects are typically closely related to the specific region. Detecting abnormal objects in the specific region is conducive to improving the accuracy of detection and analysis, thereby improving the level of logistics management. Motivated by these observations, we design the method called abnormal object detection in a specific region based on Mask R-convolutional neural network: Abnormal Object Detection in Specific Region. In this method, the initial instance segmentation model is obtained by the traditional Mask R-convolutional neural network method, then the region overlap of the specific region is calculated and the overlapping ratio of each instance is determined, and these two parts of information are fused to predict the exceptional object. Finally, the abnormal object is restored and detected in the original image. Experimental results demonstrate that our proposed Abnormal Object Detection in Specific Region can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.

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Semantic versus instance segmentation in microscopic algae detection

Cited by 87 publications

References 25 publications

Weighted Mask R‐CNN for Improving Adjacent Boundary Segmentation

Weighted Mask R‐CNN for Improving Adjacent Boundary Segmentation

Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach

Research on abnormal object detection in specific region based on Mask R-CNN

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