Gated CNN: Integrating multi-scale feature layers for object detection

•The “Gate” structure extracts powerful features for object detection.•Two-branch structure predicts the locations and categories ofobjects respectively, where each branch learns different parameters for different tasks.•The inter-class loss help detectors learn the discrepant information between categories and better differentiate similar objects of different categories•The experimental results demonstrate that G-CNN outperforms the state-of-the-art approaches, with a mAP of 40.9% at 10.6 FPS.