Detection of objects is extremely important in various aerial vision-based applications. Over the last few years, the methods based on convolution neural networks have made substantial progress. However, because of the large variety of object scales, densities, and arbitrary orientations, the current detectors struggle with the extraction of semantically strong features for small-scale objects by a predefined convolution kernel. To address this problem, we propose the rotation equivariant feature image pyramid network (REFIPN), an image pyramid network based on rotation equivariance convolution. The proposed model adopts single-shot detector in parallel with a lightweight image pyramid module to extract representative features and generate regions of interest in an optimization approach. The proposed network extracts feature in a wide range of scales and orientations by using novel convolution filters. These features are used to generate vector fields and determine the weight and angle of the highest-scoring orientation for all spatial locations on an image. By this approach, the performance for small-sized object detection is enhanced without sacrificing the performance for large-sized object detection. The performance of the proposed model is validated on two commonly used aerial benchmarks and the results show our proposed model can achieve state-of-the-art performance with satisfactory efficiency.

翻译：在各种空中视觉应用中,对物体的探测是极为重要的。过去几年来,基于卷发神经网络的方法取得了很大的进展。但是,由于物体规模、密度和任意定向的种类繁多,目前探测器与通过预先定义的卷发内核提取小型物体的精度特征的斗争正在进行。为了解决这个问题,我们提议采用旋转等离差特征图像金字塔网络(REFIPN),即基于旋转等同共变的图像金字塔网络(REFIPN)。拟议的模型采用单发探测器,同时采用轻量图像金字塔模块,以提取具有代表性的特征,并在优化方法中产生感兴趣的区域。拟议的网络通过使用新的卷发过滤器,在一系列大尺度和方向上提取特征。这些特征用于生成矢量字段,确定所有空间位置在图像上最相近相近的定位的重量和角度。通过这种方法,小型物体探测的性能得到了提高,而不会牺牲大型物体探测的性能。拟议模型的性能在两种通用航空性能基准上得到验证,并显示我们提出的结果。