AHOD: Adaptive Hybrid Object Detector for Context-Aware and real-time object detection in complex environments

This article introduces Adaptive Hybrid Object Detector (AHOD), a new paradigm in object detection designed to combine speed, accuracy and contextual adaptability. Current models, such as YOLO and Faster R-CNN, have significant limitations: YOLO excels in speed but often fails on complex objects, while Faster R-CNN prioritizes accuracy at the expense of inference time. AHOD is based on three major innovations: Feature Pyramid Enhancement (FPE), which improves multi-scale detection; Dynamic Context Module (DCM), which dynamically adjusts features according to context; and Fast and Accurate Detection Head (FADH), which balances speed and accuracy. Experimental results on COCO and Pascal VOC datasets show that AHOD outperforms existing models with an average accuracy (mAP) increase of 7%, while reducing inference time by 30%. These results demonstrate the potential of AHOD for real-time critical applications such as autonomous vehicles and surveillance.