Detection of Multiclass Objects in Optical Remote Sensing Images

Object detection in complex optical remote sensing images is a challenging problem due to the wide variety of scales, densities, and shapes of object instances on the earth surface. In this letter, we focus on the wide-scale variation problem of multiclass object detection and propose an effective object detection framework in remote sensing images based on YOLOv2. To make the model adaptable to multiscale object detection, we design a network that concatenates feature maps from layers of different depths and adopt a feature introducing strategy based on oriented response dilated convolution. Through this strategy, the performance for small-scale object detection is improved without losing the performance for large-scale object detection. Compared to YOLOv2, the performance of the proposed framework tested in the DOTA (a large-scale data set for object detection in aerial images) data set improves by 4.4% mean average precision without adding extra parameters. The proposed framework achieves real-time detection for
$1024\times 1024$
image using Titan Xp GPU acceleration.
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https://github.com/WenchaoliuMUC/Detection-of-Multiclass-Objects-in-Optical-Remote-Sensing-Images