Single-stage object detector with local binary pattern for remote sensing images

The objects in remote sensing images usually have complex background and appear anywhere in any direction, which poses challenges for deep learning-based object detection. In order to solve the above problems, we embed the local binary pattern (LBP) into deep learning-based object detection to facilitate performance improvements. Specifically, we implement thep LBP via depthwise separable convolution without any learnable parameters, which makes LBP highlight the object-related regions of feature map instead of original images. The convolutional implementation of LBP makes it easy to embed it in deep learning-based object detection, so that deep learning-based object detection can possess the ability of LBP to extract the texture information from complex background without having to learn. The gradients of the branch where LBP convolution is located are blocked and cannot be backpropagated according to proposed LBP convolution, so we propose an effective residual block, called LBPRes (Residual block with Local Binary Pattern) for normal training. To deal with the problem of object direction changes, we introduce the rotation invariance into the LBPRes, denoted as RILBPRes (LBPRes with Rotation Invariance), which makes deep learning-based object detection have the local rotation invariance to cope with the object direction changes. Finally, we construct a single-stage object detection network S(2)LBPNet (Single Stage object detection Network with Local Binary Pattern) with a local binary pattern and conduct related experiments on the DIOR and HRRSD datasets. According to the experimental results, with a small number of parameters added, S(2)LBPNet outperforms the YOLOv5s baseline by 2.2% and 5.1% on DIOR and HRRSD datasets, respectively, which prove the superiority of the proposed S(2)LBPNet.