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How can robots accurately detect and segment objects in complex environments?

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Robots need to perceive and understand their surroundings in order to perform tasks such as manipulation, navigation, and interaction. However, real-world environments are often cluttered, dynamic, and ambiguous, posing challenges for robot vision and perception. How can robots accurately detect and segment objects in complex environments? In this article, you will learn about some of the methods and techniques that control engineers use to enable robots to see and recognize objects in various scenarios.

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1 Object detection

Object detection is the process of locating and identifying objects of interest in an image or a video. It usually involves two steps: generating candidate regions that may contain objects, and classifying them into categories. There are different methods for generating regions, such as sliding windows, selective search, or region proposal networks. For classification, deep neural networks are often used to learn features and labels from large datasets of annotated images. Object detection can help robots to focus on relevant parts of the scene and avoid obstacles or distractions.

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Shyam Prasad Keshavmurthy,

Principal Application Engineer @ MathWorks ; Managing Partner @ Demyaconsulting LLC | HBAP | Computer Vision Professional
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In certain implementations using simulated and real data to train YOLO based detectors along with multi frame score helps robust detection.

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2 Object segmentation

Object segmentation is the process of separating objects from the background and from each other in an image or a video. It can be seen as a finer-grained version of object detection, where each pixel is assigned a label or a mask. Object segmentation can be divided into two types: semantic segmentation and instance segmentation. Semantic segmentation assigns the same label to all pixels that belong to the same category, regardless of whether they are different instances of the same object or not. Instance segmentation assigns a unique label to each instance of an object, distinguishing between multiple objects of the same category. Object segmentation can help robots to measure the shape, size, and pose of objects and manipulate them accordingly.

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Shyam Prasad Keshavmurthy,

Principal Application Engineer @ MathWorks ; Managing Partner @ Demyaconsulting LLC | HBAP | Computer Vision Professional
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Contribution of appropriate lighting to aid segmentations cannot be underestimated, this makes algorithmic approaches computationally less expensive, using Deep learning approaches like salsa next helps as well.

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3 Challenges and solutions

Object detection and segmentation can be tricky, especially in complex environments where objects may be occluded, overlapping, deformed, or partially visible. To address these challenges, control engineers employ various techniques and strategies like data augmentation to increase diversity and quantity of training data. Multi-scale inference processes images at different resolutions or scales to capture different details and contexts. Attention mechanisms focus on the most relevant features or regions of the images while contextual information uses additional information such as depth, motion, or scene layout to improve accuracy and robustness. Active perception also controls the robot's sensors or actuators to change the viewpoint or the illumination of the scene and acquire more information about the objects.

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4 Applications and examples

Object detection and segmentation have many applications and examples in control engineering, such as industrial robots detecting and segmenting parts, products, or tools in manufacturing or assembly lines for tasks like picking, placing, sorting, or inspecting. Service robots can use object detection and segmentation to recognize household items, furniture, or people in domestic or public settings, allowing them to perform tasks like cleaning, delivering, or assisting. Autonomous vehicles can use object detection and segmentation to detect road signs, traffic lights, pedestrians, or other vehicles in urban or rural environments to drive, park, or avoid collisions. Lastly, medical robots can use object detection and segmentation to identify organs, tissues, or lesions in medical images or videos for diagnosis, surgery, or therapy.

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5 Future trends and directions

Object detection and segmentation are still active and evolving research areas in control engineering, with many open problems and opportunities for improvement and innovation. Future trends include few-shot learning, which involves learning to detect and segment new objects from few or no examples, as well as zero-shot learning, which involves learning to detect and segment unseen objects from their semantic attributes or descriptions. Self-supervised learning is another trend, allowing for the detection and segmentation of objects without human annotations. Generative models are also becoming popular, enabling the generation of realistic and diverse images or videos of objects. Finally, explainable models are becoming more prevalent, providing visualizations, saliency maps, or natural language to explain the reasoning and decisions of object detection and segmentation models.

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