Object Detection In Underwater Acoustic Images Using Edge Based Segmentation Method

Priyadharsini, R.; Sharmila, T. Sree

doi:10.1016/j.procs.2020.01.015

Cited by 33 publications

(19 citation statements)

References 20 publications

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“…The color image needs to be converted into a grayscale image using (1). Each color (R = Red, G = Green, B = Blue) is performed with a conventional formula constant.…”

Section: Image Preprocessingmentioning

confidence: 99%

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Fish detection using morphological approach based-on k-means segmentation

2021

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Image segmentation is a concept that is often used for object detection. This detection has difficulty detecting objects with backgrounds that have many colors and even have a color similar to the object being detected. This study aims to detect fish using segmentation, namely segmenting fish images using k-means clustering. The segmentation process is processed by improving the image first. The initial process is preprocessing to improve the image. Preprocessing is done twice, before segmentation using k-means and after. Preprocessing stage 1 using resize and reshape. Whereas after k-means is the contrast-limited adaptive histogram equalization. Preprocessing results are segmented using k-means clustering. The K-means concept classifies images using segments between the object and the background (using k = 8). The final step is the morphological process with open and close operations to obtain fish contours using black and white images based on grayscale images from color images. Based on the experimental results, the process can run well, with the ssim value close to 1, which means that image information does not change. Processed objects provide a clear picture of fish objects so that this k-means segmentation can help detect fish objects.

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“…The color image needs to be converted into a grayscale image using (1). Each color (R = Red, G = Green, B = Blue) is performed with a conventional formula constant.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…Object detection can be done using the segmentation method [1]. This detection can differentiate between objects and backgrounds [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

Fish detection using morphological approach based-on k-means segmentation

2021

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“…Step4: Calculate the distance from each sample x i to the cluster center according to the Eg. ( 6), (7).…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Image segmentation is carried out in order to decompose the image into several areas with different characteristics, with the same or similar image characteristics in each area. Commonly used methods in image segmentation include threshold-based segmentation [4,5] and edge-based segmentation [6,7]. Commonly used edge detection operators include Canny operator, Sobel operator, and Roberts operator.…”

Section: Introductionmentioning

confidence: 99%

SK-FMYOLOV3: A Novel Detection Method for Urine Test Strips

Yang

Zhang

Deng

et al. 2020

Wireless Communications and Mobile Computing

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To accurately detect small defects in urine test strips, the SK-FMYOLOV3 defect detection algorithm is proposed. First, the prediction box clustering algorithm of YOLOV3 is improved. The fuzzy C-means clustering algorithm is used to generate the initial clustering centers, and then, the clustering center is passed to the K-means algorithm to cluster the prediction boxes. To better detect smaller defects, the YOLOV3 feature map fusion is increased from the original three-scale prediction to a four-scale prediction. At the same time, 23 convolutional layers of size 3 × 3 in the YOLOV3 network are replaced with SkNet structures, so that different feature maps can independently select different convolution kernels for training, improving the accuracy of defect classification. We collected and enhanced urine test strip images in industrial production and labeled the small defects in the images. A total of 11634 image sets were used for training and testing. The experimental results show that the algorithm can obtain an anchor frame with an average cross ratio of 86.57, while the accuracy rate and recall rate of nonconforming products are 96.8 and 94.5, respectively. The algorithm can also accurately identify the category of defects in nonconforming products.

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“…Recently, semantic image segmentation models were developed using a feature fusing model [11,13,15]. Alternatively, the edge-detection background segmentation algorithm based on the local maximum of the image gradient can reflect the spatial details of the images that comprise sharp edges and little noise within the smoothing region of the image [30]. In cases where underwater images normally present blurred or discontinuous edges with increased noise, it is difficult to achieve a good segmentation result using the edge detection-based segmentation model.…”

Section: Introductionmentioning

confidence: 99%

An improved K-means algorithm for underwater image background segmentation

Chen

et al. 2021

Multimed Tools Appl

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Conventional algorithms fail to obtain satisfactory background segmentation results for underwater images. In this study, an improved K-means algorithm was developed for underwater image background segmentation to address the issue of improper K value determination and minimize the impact of initial centroid position of grayscale image during the gray level quantization of the conventional K-means algorithm. A total of 100 underwater images taken by an underwater robot were sampled to test the aforementioned algorithm in respect of background segmentation validity and time cost. The K value and initial centroid position of grayscale image were optimized. The results were compared to the other three existing algorithms, including the conventional K-means algorithm, the improved Otsu algorithm, and the Canny operator edge extraction method. The experimental results showed that the improved K-means underwater background segmentation algorithm could effectively segment the background of underwater images with a low color cast, low contrast, and blurred edges. Although its cost in time was higher than that of the other three algorithms, it none the less proved more efficient than the time-consuming manual segmentation method. The algorithm proposed in this paper could potentially be used in underwater environments for underwater background segmentation.

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Object Detection In Underwater Acoustic Images Using Edge Based Segmentation Method

Cited by 33 publications

References 20 publications

Fish detection using morphological approach based-on k-means segmentation

Fish detection using morphological approach based-on k-means segmentation

SK-FMYOLOV3: A Novel Detection Method for Urine Test Strips

An improved K-means algorithm for underwater image background segmentation

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