Comparison of Target Detection Performance for Radiance and Reflectance Domain in VNIR Hyperspectral Images

Özdil, Ömer; Güneş, Ahmet; Esin, Yunus Emre; Demirel, Berkan; Ozturk, Safak

doi:10.1109/igarss.2019.8899139

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…It uses different targets and environments for making the model more effective. 7 Two types of image detection is performed over the images namely foreground level and background level. Foreground image detection suffers continuous changes in the image sequence, so it is handled by implementing region level segmentation.…”

Section: Related Workmentioning

confidence: 99%

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Object detection and estimation: A hybrid image segmentation technique using convolutional neural network model

Sundaram

Sakthivel

2022

Concurrency and Computation

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Object detection from image is more challenging and integral part in the inter-discipline area of computer vision. The computer vision is highly attractive in many applications like human pose estimation, instance segmentation, recognizing action, disease predictions object prediction and many more applications. The traditional method of detecting objects from the images is done using bounding boxes with labels. It suffers from the overlapping of the boxes with various smaller objects, which leads to accuracy issues in detection problems. Hence, machine learning techniques are used to detect the relevant objects from the image using center point to avoid the nonmaximal suppression in bounding box. To accurately identify images, an U-Net architecture based object detection method is proposed. In this model, it effectively uses semantic level segmentation and instance segmentation. This system effectively identifies all the objects present in the given image using the efficient hybrid segmentation models and Gromov Hausdroff distance measure. For experimentation, two data sets are used for evaluation of the model to identify all categories of objects from the image. The proposed model achieves an accuracy of 91.8% and reliable when compared to existing effective object detection algorithms like fully convolution network (FCN), YOLO (you only look once) and mask region based-convolutional neural network (mask R-CNN) model.

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Section: Related Workmentioning

confidence: 99%

“…RMSE measures the closeness of the pixel with other pixels in the images as specified in Equation ( 6). MAE is used to measure the distance between two variables in Equation (7). Equation ( 8) provides the MAPE measure for defining accuracy of the model on the dataset.…”

Section: Comparison Of Segmentation Algorithmsmentioning

confidence: 99%