Efficient Single Object Detection on Image Patches with Early Exit Enhanced High-Precision CNNs

• URL: http://arxiv.org/abs/2309.03530v1
• Date: Thu, 7 Sep 2023 07:23:55 GMT
• Title: Efficient Single Object Detection on Image Patches with Early Exit Enhanced High-Precision CNNs
• Authors: Arne Moos
• Abstract summary: This paper proposes a novel approach for detecting objects using mobile robots in the context of the RoboCup Standard Platform League. The challenge lies in detecting a dynamic object in varying lighting conditions and blurred images caused by fast movements. To address this challenge, the paper presents a convolutional neural network architecture designed specifically for computationally constrained robotic platforms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a novel approach for detecting objects using mobile robots in the context of the RoboCup Standard Platform League, with a primary focus on detecting the ball. The challenge lies in detecting a dynamic object in varying lighting conditions and blurred images caused by fast movements. To address this challenge, the paper presents a convolutional neural network architecture designed specifically for computationally constrained robotic platforms. The proposed CNN is trained to achieve high precision classification of single objects in image patches and to determine their precise spatial positions. The paper further integrates Early Exits into the existing high-precision CNN architecture to reduce the computational cost of easily rejectable cases in the background class. The training process involves a composite loss function based on confidence and positional losses with dynamic weighting and data augmentation. The proposed approach achieves a precision of 100% on the validation dataset and a recall of almost 87%, while maintaining an execution time of around 170 $\mu$s per hypotheses. By combining the proposed approach with an Early Exit, a runtime optimization of more than 28%, on average, can be achieved compared to the original CNN. Overall, this paper provides an efficient solution for an enhanced detection of objects, especially the ball, in computationally constrained robotic platforms.

Related papers

• Enhancing Digital Hologram Reconstruction Using Reverse-Attention Loss for Untrained Physics-Driven Deep Learning Models with Uncertain Distance [10.788482076164314]
  We present a pioneering approach to addressing the Autofocusing challenge in untrained deep-learning methods. Our method presents a significant reconstruction performance over rival methods. For example, the difference is less than 1dB in PSNR and 0.002 in SSIM for the target sample.
  arXiv  Detail & Related papers  (2024-01-11T01:30:46Z)
• EdgeYOLO: An Edge-Real-Time Object Detector [69.41688769991482]
  This paper proposes an efficient, low-complexity and anchor-free object detector based on the state-of-the-art YOLO framework. We develop an enhanced data augmentation method to effectively suppress overfitting during training, and design a hybrid random loss function to improve the detection accuracy of small objects. Our baseline model can reach the accuracy of 50.6% AP50:95 and 69.8% AP50 in MS 2017 dataset, 26.4% AP50:95 and 44.8% AP50 in VisDrone 2019-DET dataset, and it meets real-time requirements (FPS>=30) on edge-computing device Nvidia
  arXiv  Detail & Related papers  (2023-02-15T06:05:14Z)
• Feature Compression for Rate Constrained Object Detection on the Edge [20.18227104333772]
  An emerging approach to solve this problem is to offload the computation of neural networks to computing resources at an edge server. In this work, we consider a "split computation" system to offload a part of the computation of the YOLO object detection model. We train the feature compression and decompression module together with the YOLO model to optimize the object detection accuracy under a rate constraint.
  arXiv  Detail & Related papers  (2022-04-15T03:39:30Z)
• You Better Look Twice: a new perspective for designing accurate detectors with reduced computations [56.34005280792013]
  BLT-net is a new low-computation two-stage object detection architecture. It reduces computations by separating objects from background using a very lite first-stage. Resulting image proposals are then processed in the second-stage by a highly accurate model.
  arXiv  Detail & Related papers  (2021-07-21T12:39:51Z)
• Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection [4.683939045230724]
  Current object detectors are difficult to strike a balance between high accuracy and fast inference speed. We present an efficient and robust fully convolutional neural network model to perform robotic grasping pose estimation. The network is an order of magnitude smaller than other excellent algorithms.
  arXiv  Detail & Related papers  (2021-01-25T16:36:53Z)
• Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors [99.94079901071163]
  This paper presents a Dynamic Vision Sensor (DVS) based system for reasoning about high speed motion. We consider the case of a robot at rest reacting to a small, fast approaching object at speeds higher than 15m/s. We highlight the results of our system to a toy dart moving at 23.4m/s with a 24.73deg error in $theta$, 18.4mm average discretized radius prediction error, and 25.03% median time to collision prediction error.
  arXiv  Detail & Related papers  (2020-11-18T17:55:07Z)
• AmphibianDetector: adaptive computation for moving objects detection [0.913755431537592]
  We propose an approach to object detection which makes it possible to reduce the number of false-positive detections. The proposed approach is a modification of CNN already trained for object detection task. The efficiency of the proposed approach was demonstrated on the open dataset "CDNet2014 pedestrian"
  arXiv  Detail & Related papers  (2020-11-15T12:37:44Z)
• Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup [23.17839603118139]
  This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players. We propose a novel approach by formulating the problem as a segmentation task solved by an efficient CNN architecture. Our inference model can run in real time without the delay induced by a temporal analysis.
  arXiv  Detail & Related papers  (2020-07-23T09:31:32Z)
• Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation [51.17232267143098]
  We propose a novel system named Disp R-CNN for 3D object detection from stereo images. We use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds. Experiments on the KITTI dataset show that, even when LiDAR ground-truth is not available at training time, Disp R-CNN achieves competitive performance and outperforms previous state-of-the-art methods by 20% in terms of average precision.
  arXiv  Detail & Related papers  (2020-04-07T17:48:45Z)
• FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [92.48078680697311]
  Multi-object tracking (MOT) is an important problem in computer vision. We present a simple yet effective approach termed as FairMOT based on the anchor-free object detection architecture CenterNet. The approach achieves high accuracy for both detection and tracking.
  arXiv  Detail & Related papers  (2020-04-04T08:18:00Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.