Evolutionary Algorithm for Optimized CNN Architecture Search Applied to Real-Time Boat Detection in Aerial Images

Abstract

When processing the detection of boats on aerial images by neural networks, we have always been concerned about the execution time of these networks in the equipment on board the Unmanned AerialVehicle (UAV). The UAV, throughout its mission, will capture images that must be processed in realtime. For this, an execution time optimized network is essential. This article proposes a method for searching for optimized detection networks, for a given dataset, using an evolutionary algorithm. The search uses mutations as a mechanism of evolution that affect the structure of the network and the hyper-parameters of its layers. Its fitness function allows the choice of architectures that are not very greedy in terms of operations favoring small networks whose advantages are to be fast and quick to train and thus to accelerate the search algorithm. Thanks to this method we were able to obtain detection networks with performance similar to the initial network (parent) but with much fewer operations allowing considerable gains in terms of execution times in an environment embedded on a drone.