Semantic segmentation remains a computationally intensive algorithm for embedded deployment even with the rapid growth of computation power. Thus efficient network design is a critical aspect especially for applications like automated driving which requires real-time performance. Recently, there has been a lot of research on designing efficient encoders that are mostly task agnostic. Unlike image classification and bounding box object detection tasks, decoders are computationally expensive as well for semantic segmentation task. In this work, we focus on efficient design of the segmentation decoder and assume that an efficient encoder is already designed to provide shared features for a multi-task learning system. We design a novel efficient non-bottleneck layer and a family of decoders which fit into a small run-time budget using VGG10 as efficient encoder. We demonstrate in our dataset that experimentation with various design choices led to an improvement of 10% from a baseline performance.

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This paper presents a self adaptive dragonfly optimization (DFO) based methodology for performing multilevel segmentation of colour fundus images. The multilevel segmentation problem is formulated as an optimization problem and solved using the DFO. The method optimizes the threshold values for each of the three chromatic channels of colour fundus images through effectively exploring the solution space in obtaining the global best solution. The results of two fundus images illustrate the performance of the developed method. General Terms image processing, segmentationKeywords fundus images, multilevel segmentation.

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