Abstract
Recent research on convolutional neural network (CNN) has seen advances in accuracy improvements of object detection, recognition, tracking, and segmentation applications. Many CNN architectures have been developed in the last five years to achieve good amount of accuracy. To deploy trained CNN models on FPGA, Raspberry Pi and other hardware devices with reduced model size is a key area for research. In this paper, a hybrid approach for pixel level semantic segmentation using SqueezeNet and SegNet is proposed. The hybrid architecture enhances the accuracy to 76%.
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