Multi-scale entropy neural architecture search for object detection in remote sensing images

Aiming at the traditional neural architecture search requires an enormous amount of time for supernet training, search efficiency is suboptimal, and the searched model can not efficiently solve the problem of multi-scale object detection difficulty and high background complexity in remote sensing images. This paper proposes a multi-scale entropy neural architecture search method for object detection in remote sensing images. At first, the feature separation convolution is added to the base block of the search space instead of the regular convolution in the residual block, which reduces the information redundancy in remote sensing images due to the high background complexity, and improves the detection performance of the network modeluner the complex background. Next, the maximum entropy principle is introduced to calculate the multi-scale entropy of each candidate network in the search space, and the multi-scale entropy is combined with the feature pyramid network to balance the detection of large, medium and small objects in remote sensing images. Finally, the network model with maximum multi-scale entropy is obtained by searching without parameter training using progressive evolutionary algorithm for the object detection task.The model ensures detection accuracy while improving the search efficiency. The proposed algorithm achieves a mean average precision of 93.1%, 75.5% and 73.6% on the RSOD, DIOR and DOTA datasets, respectively, with a network search time of 8.1 hours. The experimental results demonstrate that the proposed algorithm can significantly improve the search efficiency of the network, combine the features at different scales better and solve the problem of high image background complexity in the object detection task compared with the current benchmark methods. © 2024 SinoMaps Press. All rights reserved.