FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving

• URL: http://arxiv.org/abs/2011.08061v1
• Date: Mon, 16 Nov 2020 16:15:43 GMT
• Title: FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving
• Authors: Seontaek Oh, Ji-Hwan You, Young-Keun Kim
• Abstract summary: We propose a lightweight one-stage object detector that is balanced to satisfy all the constraints of accuracy, model size, and real-time processing. Our network aims to maximize the compression of the model while achieving or surpassing YOLOv3 level of accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: For deployment on an embedded processor for autonomous driving, the object detection network should satisfy all of the accuracy, real-time inference, and light model size requirements. Conventional deep CNN-based detectors aim for high accuracy, making their model size heavy for an embedded system with limited memory space. In contrast, lightweight object detectors are greatly compressed but at a significant sacrifice of accuracy. Therefore, we propose FRDet, a lightweight one-stage object detector that is balanced to satisfy all the constraints of accuracy, model size, and real-time processing on an embedded GPU processor for autonomous driving applications. Our network aims to maximize the compression of the model while achieving or surpassing YOLOv3 level of accuracy. This paper proposes the Fire-Residual (FR) module to design a lightweight network with low accuracy loss by adapting fire modules with residual skip connections. In addition, the Gaussian uncertainty modeling of the bounding box is applied to further enhance the localization accuracy. Experiments on the KITTI dataset showed that FRDet reduced the memory size by 50.8% but achieved higher accuracy by 1.12% mAP compared to YOLOv3. Moreover, the real-time detection speed reached 31.3 FPS on an embedded GPU board(NVIDIA Xavier). The proposed network achieved higher compression with comparable accuracy compared to other deep CNN object detectors while showing improved accuracy than the lightweight detector baselines. Therefore, the proposed FRDet is a well-balanced and efficient object detector for practical application in autonomous driving that can satisfies all the criteria of accuracy, real-time inference, and light model size.

Related papers

• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• 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)
• Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff [26.566339984225756]
  Existing salient object detection methods often adopt deeper and wider networks for better performance. We propose a novel trilateral decoder framework by decoupling the U-shape structure into three complementary branches. We show that our method achieves better efficiency-accuracy balance across five benchmarks.
  arXiv  Detail & Related papers  (2023-01-17T03:43:25Z)
• Rethinking Voxelization and Classification for 3D Object Detection [68.8204255655161]
  The main challenge in 3D object detection from LiDAR point clouds is achieving real-time performance without affecting the reliability of the network. We present a solution to improve network inference speed and precision at the same time by implementing a fast dynamic voxelizer. In addition, we propose a lightweight detection sub-head model for classifying predicted objects and filter out false detected objects.
  arXiv  Detail & Related papers  (2023-01-10T16:22:04Z)
• Small Object Detection Based on Modified FSSD and Model Compression [7.387639662781843]
  This paper proposes a small object detection algorithm based on FSSD. In order to reduce the computational cost and storage space, pruning is carried out to achieve model compression. The average accuracy (mAP) of the algorithm can reach 80.4% on PASCAL VOC and the speed is 59.5 FPS on GTX1080ti.
  arXiv  Detail & Related papers  (2021-08-24T03:20:32Z)
• Developing a Compressed Object Detection Model based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System [0.0]
  CNN-based object detection models are quite accurate but require a high-performance GPU to run in real-time. It is preferable to compress the detection network as light as possible while preserving the detection accuracy. This paper proposes a new object detection model, referred as YOffleNet, which is compressed at a high ratio.
  arXiv  Detail & Related papers  (2021-08-01T08:19:51Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• Lite-FPN for Keypoint-based Monocular 3D Object Detection [18.03406686769539]
  Keypoint-based monocular 3D object detection has made tremendous progress and achieved great speed-accuracy trade-off. We propose a sort of lightweight feature pyramid network called Lite-FPN to achieve multi-scale feature fusion. Our proposed method achieves significantly higher accuracy and frame rate at the same time.
  arXiv  Detail & Related papers  (2021-05-01T14:44:31Z)
• AQD: Towards Accurate Fully-Quantized Object Detection [94.06347866374927]
  We propose an Accurate Quantized object Detection solution, termed AQD, to get rid of floating-point computation. Our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes.
  arXiv  Detail & Related papers  (2020-07-14T09:07:29Z)
• APQ: Joint Search for Network Architecture, Pruning and Quantization Policy [49.3037538647714]
  We present APQ for efficient deep learning inference on resource-constrained hardware. Unlike previous methods that separately search the neural architecture, pruning policy, and quantization policy, we optimize them in a joint manner. With the same accuracy, APQ reduces the latency/energy by 2x/1.3x over MobileNetV2+HAQ.
  arXiv  Detail & Related papers  (2020-06-15T16:09:17Z)

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.