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Semantic Segmentation With Oblique Convolution for Object Detection
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DOI：10.1109/ACCESS.2020.2971058.png)
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Abstract
En 中文
With the rapid development of artificial intelligence, object detection is playing an important role in the field of computer vision. Instead of anchors, we use pixel classification inspired by Semantic Segmentation to get the local extreme points of the four boundaries of an object and then the boundary positions. We calculate the possibility whether every pixel in the image is the extreme point by hourglass network. With the introduction of the mask mechanism and oblique convolution, the network has achieved better results. The experiment result shows that: it achieves an AP of 37.7% on the MS COCO dataset while costing less than 3 seconds on mobile.
Keywords:
Artificial intelligence
computer vision
convolution neural network
machine learning
neural networks
object detection
object segmentation
predictive models
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