What is Maritime AI Object Detection in International Shipping News 18/03/2026 AI object detection identifies and locates maritime objects using deep neural networks, enabling safer vessel navigation and collision avoidance. Object detection is one of the most fundamental and challenging problems in computer vision. It focuses on identifying and localising instances of predefined object classes (such as boats, humans, animals) in digital images. What is object detection? Compared to image classification, object detection provides more information about an image’s content. The following example demonstrates this. Image classification would merely answer the question, “What object is in the image?” – it would say “ship” as an answer, if the model works well. If the model can predict several classes, it might even say “ship” and “far away object”, but that is the most information we can get from it. In comparison, object detection would not only answer “What object is in the image?” but also “How many objects are in the image?” and “Where are the images located?” Of course, this is a more useful answer in many real-world scenarios, such as collision avoidance. Deep Learning These answers, also so-called predictions, are generated by models. Nowadays, the models are deep neural networks (DNNs). Types of DNNs Several types of DNNs exist, but two dominate tasks like object detection. The most foundational deep learning method for computer vision uses so-called Convolutional Neural Networks (CNNs). Researchers invented the architecture of CNNs back in 1980. However, the breakthrough paper popularizing deep CNNs appeared only in 2012, introducing AlexNet. Since then, CNNs have been a foundational ingredient in computer vision. Only a few years ago, in 2017, researchers at Google introduced the Transformer models, which became very popular for their strong detection performance. Nevertheless, they have not fully replaced CNNs, as Transformers tend to be slower, require more data for training, and require more computational power. How do DNNs work? These models consist of several layers stacked on top of one another. When you feed an image into the model, each layer processes it, and each layer feeds its output into the next. The term “deep” in deep learning refers to the presence of a large number of such layers. The figure below shows a very simplified layout of how these layers stack. Note that this is just a toy example; in practice, models contain many more layers, different kinds of layers, and other types of connections and processing operations. In a very simplified sense, one can think of each layer as a calculation that uses addition and multiplication. Each layer consists of multiple parameters, which are essentially simple numbers that these mathematical operations use. In terms of the total number of parameters per model, DNNs in object detection can vary a lot – from 2.4M parameters (YOLO26 nano, a CNN-based model) up to 218M parameters (a DINO version using a SwinL backbone, a transformer-based model). The essence of training: data To obtain a model that makes good predictions, you must train it. Intuitively, during this process, the model learns what certain types of objects look like and how to correctly detect them. In deep learning, data drives the training process. That means, instead of explicitly telling the model how certain types of objects look (essentially impossible), we give the model large amounts of data and the desi