  Object detection is widely studied in computer vision filed. In recent years,
certain representative deep learning based detection methods along with solid
benchmarks are proposed, which boosts the development of related researchs.
However, there is no object detection benchmark targeted at military field so
far. To facilitate future military object detection research, we propose a
novel, publicly available object detection benchmark in military filed called
MOD, which contains 6,000 images and 17,465 labeled instances. Unlike previous
benchmarks, objects in MOD contain unique challenges such as camouflage, blur,
inter-class similarity, intra-class variance and complex military environment.
Experiments show that under above chanllenges, existing detection methods
suffer from undesirable performance. To address this issue, we propose LGA-RCNN
which utilizes a loss-guided attention (LGA) module to highlight representative
region of objects. Then, those highlighted local information are fused with
global information for precise classification and localization. Extensive
experiments on MOD validate the effectiveness of our method.
