Multi-Object Localization and Classification, often termed Object Detection, is a pivotal task in computer vision, crucial across various industries. In automotive safety, it powers advanced driver-assistance systems to identify hazards like pedestrians and other vehicles, enhancing safety. In healthcare, object detection facilitates medical imaging by identifying anomalies such as tumors in X-rays or MRIs, leading to faster and more accurate diagnoses. In agriculture, it aids in monitoring crop health and automating harvesting processes through drones and cameras, optimizing yields and reducing labor costs. These applications are driven by object detection's role as a transformative digital technology that significantly impacts public safety, health, and food production in the future world.
Object detection involves creating bounding boxes around each object in an image and identifying their categories. Unlike single object localization that focuses on locating one item, object detection manages multiple objects in the same image, making it essential for applications that need a detailed analysis of visual data.
Example: Object Detection for a Street Scenario
Single-object localization tasks operate under the assumption of one output per sample, allowing for straightforward predictions and label comparisons, whereas object detection introduces complexity with its dynamic count of labels (ground truths about the positions of objects) per image—ranging from one object to many.
In the figures on the right, the top one demonstrates a complex object detection scenario with many labeled objects like cars and pedestrians. This showcases situations where an image contains numerous objects. In contrast, the bottom one simplifies the task with just a dog and a cat, each in its own bounding box, highlighting scenarios where images contain only a few objects.
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This raises a concern regarding the implementation of the loss function; although we can regulate the number of predictions, the ground truth labels vary across different input images.
Therefore, our loss function needs to support a varying number of ground truths. The strategy here is quite straightforward: generate more "undetected" ground truths so that each prediction has a corresponding ground truth. In later section, we will examine how to assign a ground truth to each prediction from the perspectives of two-stage and one-stage approaches.
You may hear terms like '1-Stage Detector' and '2-Stage Detector.' These terms represent fundamentally different design philosophies in the field of object detection, each with its unique approach to identifying and classifying objects within images.
2-Stage object detection models, like R-CNN, Fast R-CNN, and Faster R-CNN, operate by first generating regions of interest, then classifying and refining these regions. They excel in accuracy and adaptability across diverse object sizes and overlaps but are slower and more resource-intensive than 1-Stage models, making them less suited for real-time applications. Their advantage lies not necessarily in superior performance compared to models like YOLO but in their controllability and precision in handling complex detection tasks.
1-Stage object detection models are designed to be fast and efficient. They accomplish the task of object detection in a single shot, hence the name. This means that in one single pass through the network, these models predict both the class (what the object is) and the bounding boxes (where the object is) simultaneously. Examples of 1-Stage detectors include YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and RetinaNet.
