Scale Optimization Using Evolutionary Reinforcement Learning for Object
  Detection on Drone Imagery
Jialu Zhang Xiaoying Yang Wentao He Jianfeng Ren Qian Zhang Titian Zhao Ruibin Bai Xiangjian He Jiang Liu
Object detection in aerial imagery presents a significant challenge due to
large scale variations among objects. This paper proposes an evolutionary
reinforcement learning agent, integrated within a coarse-to-fine object
detection framework, to optimize the scale for more effective detection of
objects in such images. Specifically, a set of patches potentially containing
objects are first generated. A set of rewards measuring the localization
accuracy, the accuracy of predicted labels, and the scale consistency among
nearby patches are designed in the agent to guide the scale optimization. The
proposed scale-consistency reward ensures similar scales for neighboring
objects of the same category. Furthermore, a spatial-semantic attention
mechanism is designed to exploit the spatial semantic relations between
patches. The agent employs the proximal policy optimization strategy in
conjunction with the evolutionary strategy, effectively utilizing both the
current patch status and historical experience embedded in the agent. The
proposed model is compared with state-of-the-art methods on two benchmark
datasets for object detection on drone imagery. It significantly outperforms
all the compared methods.