Object detection is a critical task in computer vision with wide-ranging applications, from autonomous driving tosurveillance systems. Despite notable progress, challenges such as detecting small objects, managing occlusions,and effectively integrating multiscale features persist. We propose RetinaGate, a novel object detection architec-ture that introduces a Gated Feature Pyramid Network (G-FPN) to adaptively fuse multi-scale features, enhancedby Squeeze-and-Excitation-based channel attention for improved accuracy. As a plug-and-play module, G-FPNcan be seamlessly integrated into existing detection models to enhance their accuracy. These enhancementsstrengthen the model’s capacity to capture fine-grained details and leverage contextual information more effec-tively. Experimental results on three benchmark datasets demonstrate that RetinaGate outperforms the baselineRetinaNet in terms of detection accuracy, particularly in challenging detection scenarios such as underwater.