AHOD: Adaptive Hybrid Object Detector for Context-Awareed Item

We evaluated our AHOD model using two well-known datasets in the field of object detection:<b>COCO (Common Objects in Context)</b>One of the most widely used benchmarks for object detection.Contains over 200,000 images and more than 80 object categories.<br>Includes objects in varied and sometimes cluttered contexts, allowing the robustness of detectors to be evaluated.<b>Pascal VOC</b>Another reference dataset, often used for classification, detection and segmentation tasks.Includes 20 object categories, with precise bounding box annotations.<br>Less complex than COCO, but useful for comparing performance on more conventional objects.<b>Tools, techniques and innovations used</b>The AHOD architecture is based on <b>three main modules</b>:<b>Feature Pyramid Enhancement (FPE)</b>Multi-scale feature processing tool.Improves the representation of objects of various sizes in the same image.<br>Inspired by architectures such as FPN (Feature Pyramid Networks), but optimised for better performance.<b>Dynamic Context Module (DCM)</b>Intelligent contextual module.Capable of dynamically adjusting the extracted features according to the context (e.g. by adapting the features according to urban or rural areas in a road image).Enhances the model's ability to understand the overall context of the scene.<br><b>Fast and Accurate Detection Head (FADH)</b>Optimised detection head.Seeks a compromise between the speed of YOLO and the accuracy of Faster R-CNN.Probably uses lightweight convolution layers or optimisations such as MobileNet/Depthwise Convolutions.<b>Probable technologies used</b><br>Although the summary does not specify this, we can reasonably assume that the following tools are used:<b>Deep learning frameworks</b>: PyTorch or TensorFlow, which are standard in object detection research.<b>GPUs</b> for training and inference, particularly for measuring inference times (essential in real-time applications).<b>Standard evaluation techniques</b>:<b>mAP (mean Average Precision)</b>: measure of average precision.<b>FPS (Frames Per Second)</b> or <b>inference time</b> for real-time performance.