Collaborative Deep Reinforcement Learning for Joint Object Search

We examine the problem of joint top-down active search of multiple objects under interaction, e.g., person riding a bicycle, cups held by the table, etc.. Such objects under interaction often can provide contextual cues to each other to facilitate more efficient search. By treating each detector as an agent, we present the first collaborative multi-agent deep reinforcement learning algorithm to learn the optimal policy for joint active object localization, which effectively exploits such beneficial contextual information. We learn inter-agent communication through cross connections with gates between the Q-networks, which is facilitated by a novel multi-agent deep Q-learning algorithm with joint exploitation sampling. We verify our proposed method on multiple object detection benchmarks. Not only does our model help to improve the performance of state-of-the-art active localization models, it also reveals interesting co-detection patterns that are intuitively interpretable

Incremental Learning for Robot Perception through HRI

Scene understanding and object recognition is a difficult to achieve yet crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have shown success in this task. However, there is still a gap between their performance on image datasets and real-world robotics scenarios. We present a novel paradigm for incrementally improving a robot's visual perception through active human interaction. In this paradigm, the user introduces novel objects to the robot by means of pointing and voice commands. Given this information, the robot visually explores the object and adds images from it to re-train the perception module. Our base perception module is based on recent development in object detection and recognition using deep learning. Our method leverages state of the art CNNs from off-line batch learning, human guidance, robot exploration and incremental on-line learning

A Survey on Object Recognition Using Deep Neural Networks

Deep Neural Networks as a means of objects detection and recognition is an active area of research and several discoveries have been made in this field. Here we will be discussing briefly about the history of research in the field of computer vision, mainly for the application of deep learning in object detection task and describe several of the recent advances in this ?eld. This paper describes a simple summary of the datasets and deep learning algorithms commonly used in computer vision, some of the applications of this ?eld have been provided

Automated Identification of Wood Surface Defects Based on Deep Learning

Wood plates are widely used in the interior design of houses primarily for their aesthetic value. However, considering its esthetical values, surface defect detection is necessary. The development of computer vision and CNN-based object detection methods has opened the way for wood surface defect detection process automation. This paper investigates deep-learning applications for automatic wood surface defect detection. It includes the evaluation of deep learning algorithms, including data generation and labeling, preprocessing, model training, and evaluation. Many adjustments regarding the dataset size, the model, and the modification of the neural network were made to evaluate the model's performance in the specified challenge. The results indicate that modifications can increase the YOLOv5s performance in detection. The model with GCNet added and trained in 4800 images has achieved 88.1% of mAP. The paper also evaluates the time performance of models based on different GPU units. The results show that in A100 40GB GPU, the maximum time to process a wood plate is 2.2 seconds. Finally, an Active learning approach for the continual increase in performance while detecting with the smaller size of manual labeling has been implemented. After detecting 500 images in 5 cycles, the model achieved 98.8% of mAP. This scientific paper concludes that YOLOv5s modified model is suitable for wood surface defect detection. It can perform with high accuracy in real time. Moreover, applying the active learning approach can facilitate the labeling process by increasing the performance during detection