Zhipeng Deng

TL;DR: This paper proposes a unified and effective method for simultaneously detecting multi-class objects in remote sensing images with large scales variability, and shows that the method is more accurate than existing algorithms and is effective for multi-modalRemote sensing images.

TL;DR: An improved detection method based on Faster R-CNN is proposed, which employs a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps and replaces the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions.

TL;DR: To accurately extract vehicle-like targets, an accurate-vehicle-proposal-network (AVPN) based on hyper feature map which combines hierarchical feature maps that are more accurate for small object detection is developed and a coupled R-CNN method is proposed, which combines an AVPN and a vehicle attribute learning network to extract the vehicle's location and attributes simultaneously.

TL;DR: This paper presents an end-to-end single convolutional neural network to generate arbitrarily-oriented vehicle detection results directly, and uses a set of default boxes with various scales on each feature map location to produce detection bounding boxes.

TL;DR: This paper designs a condensed backbone network, which consists of several dense blocks, and improves the cross-entropy loss to address the foreground–background imbalance and predict multi-scale ship proposals from several intermediate layers to improve the recall rate.