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

Abstract

When processing the detection of boats in aerial images by neural networks, we have always been concerned about the execution time of these networks in the equipment on board the Unmanned Aerial Vehicle (UAV). Throughout its mission, the UAV will capture images that must be processed in real time. For this purpose, a network optimized for execution time is essential. This article proposes an enhanced Network Architecture Search (NAS) method for searching for time-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 original fitness function allows the choice of architectures that are not very greedy in terms of operations, specifically favouring small networks whose advantages are to be fast and quick to train, thus accelerating the search algorithm. Using this method, we were able to obtain detection networks with an improved mean Average Precision (mAP) compared to the initial network (parent) but with much fewer FLoating-point OPerations (Flops): 68% of operations reduction. This induces considerable gain in terms of execution time with 50 Frames Processed per Second (FPS) in an embedded environment on a drone.