![]() ![]() The designed models are incorporated into an end-to-end pose estimation pipeline based on Unity and ROS Noetic, where a UR3 Robotic Arm is deployed in a simulated pick-and-place task. In this work, we develop a series of convolutional neural network-based pose estimation models without post-refinement stages, designed to achieve high accuracy on relevant metrics for efficiently estimating the 6D pose of an object, using only a single RGB image. The sensory data can include RGB images and data from depth sensors, but determining the object’s pose using only a single RGB image is cost-effective and highly desirable in many applications. To realise this task of autonomous object grasping, one of the critical sub-tasks is the 6D Pose Estimation of a known object of interest from sensory data in a given environment. The ability of a robot to sense and “perceive" its surroundings to interact and influence various objects of interest by grasping them, using vision-based sensors is the main principle behind vision based Autonomous Robotic Grasping. Features extracted through the model are clustered using t-SNE visualization technique to demonstrate the model’s efficacy in distinguishing features of smoke and non-smoke images. Its performance is also compared with eight existing deep learning smoke detection models that shows its superiority over other models. ![]() The model is lightweight with only 1.23 million parameters, reasonably lower than the existing deep learning models. In this paper, a convolutional neural network with attention mechanism and residual learning is proposed for smoke detection using images of outdoor scenes. Further, deep learning models have large memory footprint that hinders their usage in IoT based smoke detection systems. Despite advancements in the field, smoke detection in challenging environments is still a concern in real time applications. Recent techniques deploy deep learning models for smoke detection in an outdoor environment. Therefore smoke detection using vision based machine learning techniques have been quite useful. An upward smoke movement can help identify location of a fire incident. Fire hazards have increased in recent years and detecting fire at an early stage is of utmost importance. ![]()
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