DETration TRansformer (DETR) and You Only Look Once (YOLO) are two object detection methods. YOLO holds an unparalleled position in real-time object detection and tracking problems. At the same time, DETR, the pioneering work that introduced the Transformer model into the field of object detection, utilizes the self attention mechanism of the Transformer structure to encode each object, relies on its parallelism, constructs an end-to-end detection model, and avoids redundant operations of various types in previous models, making object detection problems simpler.
Since both have their own advantages, why do visual engineers of inspection robots generally use the YOLO algorithm?
The original paper published by YOLO was a breakthrough in real-time object detection, and it remains one of the most commonly used models in practical visual applications. It shifts the detection process from two or three stages (i.e. R-CNN, Fast R-CNN) to a single-stage convolution stage, and outperforms all state-of-the-art object detection methods in terms of accuracy and speed. Over time, the model architecture in the original paper has changed, adding different manual designs to improve the accuracy of the model.
DETR (Detection TRansformer) is a relatively new object detection algorithm launched by researchers from Facebook AI Research (FAIR) in 2020. It is based on the Transformer architecture, which is a powerful sequence to sequence model that has been used for various natural language processing tasks. Traditional object detectors (i.e. R-CNN and YOLO) are complex, while the DETR architecture is simple, consisting of three main components: a CNN backbone network (i.e. ResNet) for feature extraction, a Transformer encoder decoder, and a feedforward neural network (FFN) for final detection and prediction. The backbone processes input images and generates activation maps. The Transformer encoder reduces the channel size and applies multi head self attention and feedforward networks. The Transformer decoder uses parallel decoding with N object embeddings and queries independent predicted box coordinates and class labels using objects.
Through the above comparison, it can be seen that YOLO is superior to DETR in completing detection tasks with fast speed, good real-time performance, and high accuracy. And this advantage is very important for inspection robots.
The NavBot robot development team has developed autonomous algorithms suitable for various scenarios based on actual situations. Based on the convolutional neural network deep recognition algorithm, the accuracy of selecting target points is greater than 96%, enabling the detection robot to upload detection data in real time during operation, with short time consumption, strong recognition ability, and also suitable for old equipment.
NavBot robot is driven by technological innovation as its core driving force, and its research and development team has decades of industry experience, dedicated to continuous innovation and breakthroughs in the field of intelligent detection robots. The company has established an efficient and innovative technology team, with core team members deeply involved in fields such as visual intelligence analysis, natural language processing, positioning, and navigation.
After years of development, our customer base covers large enterprises such as State Grid, China National Energy Corporation, PetroChina, major water management groups, Agricultural Bank of China, and China Mobile. We have provided intelligent inspection solutions for over a hundred partners in scenarios such as computer rooms, distribution rooms, substations, coal transportation corridors, underground corridors, and oil and gas chemical plant stations, with profound industry implementation experience.
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