ApesNet: A pixel-wise efficient segmentation network
Autonomous driving can effectively reduce traffic congestion and road accidents. Therefore, it is necessary to implement an efficient high-level, scene understanding model in an embedded device with limited power and sources. Toward this goal, we propose ApesNet, an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. The key findings in our experiments are significantly lower the classification time and achieve a high accuracy compared to other conventional segmentation methods. The model is characterized by an efficient training and a sufficient fast testing. Experimentally, we use the well-known CamVid road scene dataset to show the advantages provided by our contributions. We compare our proposed architecture's accuracy and time performance with SegNet. In CamVid dataset training and testing, our network, ApesNet outperform SegNet in eight classes accuracy. Additionally, our model size is 37% smaller than SegNet. With this advantage, the combining encoding and decoding time for each image is 1.45 to 2.47 times faster than SegNet.
