{"@attributes":{"version":"2.0"},"channel":{"title":"Object-Detection on Oriol Al\u00e0s Cerc\u00f3s","link":"https:\/\/oriolac.github.io\/tags\/object-detection\/","description":"Recent content in Object-Detection on Oriol Al\u00e0s Cerc\u00f3s","generator":"Hugo -- 0.150.0","language":"en-us","copyright":"Oriol Al\u00e0s Cerc\u00f3s","lastBuildDate":"Sat, 25 Apr 2026 20:10:23 +0100","item":{"title":"Reviewing YOLO: You Only Look Once","link":"https:\/\/oriolac.github.io\/posts\/20260501-yolo\/","pubDate":"Sat, 25 Apr 2026 20:10:23 +0100","guid":"https:\/\/oriolac.github.io\/posts\/20260501-yolo\/","description":"<p>Object detection is one of the most popular tasks in computer vision, since it can be applied to a wide range of\napplications: robotics, autonomous driving or fault detection. In this post, we will try to give a brief overview of\nthe YOLO algorithm and the components that make it work.<\/p>\n<p>To do that, I have classified the main components of the algorithm into three categories:<\/p>\n<ul>\n<li>Characteristics based on the <strong>model architecture<\/strong>, how YOLO-based models improved the performance by using a new\narchitecture and which are the improvements made.<\/li>\n<li>Strategies based on the <strong>model training<\/strong>, such as the function loss or data augmentation.<\/li>\n<li>Methods for <strong>post-processing the output<\/strong> of the model, such as the non-maximum suppression (NMS) and the\nconfidence threshold.<\/li>\n<\/ul>\n<h2 id=\"two-stage-vs-one-stage-detectors\">Two-stage vs One-stage Detectors<\/h2>\n<p>Before YOLO, SoTA detectors were based on a <strong>two-stage detector<\/strong>: the first stage is used to detect the bounding\nboxes,\nand the second stage is used to classify the bounding boxes. This kind of model is called region-based detectors,\nbecause they need the region to then run the classification.<\/p>"}}}