Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation

Semantic Segmentation using deep convolutional neural network pose more complex challenge for any GPU intensive work, as it has to compute million of parameters resulting to huge consumption of memory. Moreover, extracting finer features and conducting supervised training tends to increase the complexity furthermore. With the introduction of Fully Convolutional Neural Network, which uses finer strides and utilizes deconvolutional layers for upsampling, it has been a go to for any image segmentation task. We propose two segmentation architecture transferring weights from the popular classification neural net VGG19 and VGG16 which were trained on Imagenet classification dataset, transform all the fully connected layers to convolutional layers, use dilated convolution for decreasing the parameters, moreover we add more finer strides and attach four skip architectures which are concatenated with the deconvolutional layers in steps. We train on two stages, first with PASCAL VOC2012 training data and then with SBD training and validation set. With our model, FCN-2s-Dilated-VGG19 we yield better score for PASCAL VOC2012 test set with a meanIOU of 69 percent which is 1.8 percent better than FCN-8s. And with FCN-2s-Dilated-VGG16 we score a meanIOU of 67.6 percent. On the other hand our model consumes up to 10-20 percent less memory than FCN-8s for training with NVIDIA Pascal GPUs, making it more efficient and less memory consuming architecture for pixel-wise segmentation. Keywords— Deep Learning, Convolutional Neural Network, Semantic Image Segmentation, Skip Architectures;