U-In-Effnet: Semantic Segmentation With The Effect Of Magnifying Glasss
Keywords:
Unet, EfficientNet, Semantic Segmentation, CNNAbstract
Each of the growing advancement in the field of Semantic segmentation has taken us a step ahead towards effectively achieving Artificial Intelligence. CNN has again and again proved its semantic competency in extracting rich features at various level of abstraction. Semantic segmentation of the image for better understanding of the objects and its context is essential for wide range of applications ranging from scene classification to automatic driving vehicles. An encoder- decoder inspired U-net architecture using Efficient Net as backbone is been proposed. To replicate the effect of magnifying glass for diverse feature rich extraction inception blocks with different kernel size is added into decoder. The proposed method is tested on the most unstructured, delineated Indian Driving Dataset (IDD) and the popular benchmark Pascal VOC 2012 dataset. The architecture with its well defined segmentation map outperforms the previous benchmark results by attaining mean Intersection over Union (mIoU) of 0.78 and 0.63 on Pascal VOC and IDDLite dataset respectively.
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