Object detection with vector representations relies on converting visual data into numerical vectors that capture meaningful features of objects in an image. This process typically starts with a convolutional neural network (CNN) backbone, which processes the input image to generate a feature map. Each position in this feature map corresponds to a region in the original image and is represented as a vector encoding spatial and semantic information. For example, in a ResNet-based model, deeper layers produce vectors that capture higher-level features like shapes or textures, while earlier layers detect edges or colors. These vectors serve as the foundation for identifying and locating objects.
The next step involves using these vector representations to predict object classes and bounding boxes. Models like Faster R-CNN or YOLO apply specialized heads (networks) to the feature vectors to make these predictions. For instance, in a one-stage detector like YOLO, the feature map is divided into grid cells. Each cell’s vector is used to predict whether an object exists in that region, its class (e.g., “car” or “person”), and the coordinates of a bounding box around it. Anchor boxes—predefined template shapes—are often used to refine these predictions. The model compares the vector’s features to the anchors and adjusts their size and position via regression. This approach allows the system to handle objects of varying scales and aspect ratios efficiently.
Finally, post-processing techniques like non-maximum suppression (NMS) clean up overlapping predictions. For example, if two bounding boxes for the same object have 90% overlap, NMS keeps the one with the highest confidence score. Vector representations also enable tasks like instance segmentation (using Mask R-CNN), where pixel-level masks are predicted alongside bounding boxes. Developers can implement these workflows using frameworks like TensorFlow or PyTorch, often leveraging pre-trained models fine-tuned on custom datasets. A practical example is training a model to detect retail products on shelves: the vectors encode product-specific features (logos, packaging), and the detector localizes each item while classifying it. This combination of feature encoding and prediction heads makes vector-based object detection both flexible and scalable.