Fast Armored Target Detection Based on Lightweight Network

Focused on the detection task of armored target in battlefield environment, a fast detection method based on lightweight convolutional neural network is proposed in this paper. Firstly, based on the lightweight backbone network (MobileNet), a multi-scale single-stage detection framework is developed. Secondly, considering the size distribution of armored target, higher resolution feature maps are selected and a new designed Resblock is added to each detection unit to enhance the detection performance for small targets. At last, focal-loss function is introduced to replace the traditional cross entropy loss function, which effectively overcomes the extreme imbalance of the distribution of the positive and negative samples in training processes. A special detection dataset for armored target is constructed, based on which the comparable experiments with state-of-art detection methods are conducted. Experimental results show that the proposed method achieves good performance in detection accuracy, model size and operation speed, and is especially suitable for small mobile reconnaissance platforms such as UAVs (unmanned aerial vehicle).