YOLORS-LITE: A LIGHTWEIGHT CNN FOR REAL-TIME OBJECT DETECTION IN REMOTE-SENSING

Detection CNN architectures often exhibit over-parameterization which results in excessive computational and storage overhead, but also undesired overfitting and reduced performance. In this work we focus on YOLOrs, a state-of-the-art CNN for target detection in remote sensing imagery, and counteract over-parameterization by enforcing Tensor-Train (TT) structure to its convolutional kernels. While TT has been successfully used before for compressing classification CNNs, this work is the first one that uses it to compress a detection CNN. We refer to the resulting network as YOLOrs-lite and compare its performance against standard YOLOrs as well as other state-of-the-art detection networks. Our numerical studies show that the proposed network attains superior detection performance, with storage savings as high as 70%. The proposed network combines light storage with real-time inference, making it quite promising for edge deployment.