Research on Optimization Method of Multi-scale Fish Target Fast Detection Network

• URL: http://arxiv.org/abs/2104.05050v1
• Date: Sun, 11 Apr 2021 16:53:34 GMT
• Title: Research on Optimization Method of Multi-scale Fish Target Fast Detection Network
• Authors: Yang Liu, Shengmao Zhang, Fei Wang, Wei Fan, Guohua Zou, Jing Bo
• Abstract summary: The accuracy of testing the network with 2000 fish images reached 94.37%, and the computational complexity of the network BFLOPS was only 5.47. The results show that BTP-Yolov3 has smaller model parameters, faster calculation speed, and lower energy consumption during operation.
• Score: 11.99307231512725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The fish target detection algorithm lacks a good quality data set, and the algorithm achieves real-time detection with lower power consumption on embedded devices, and it is difficult to balance the calculation speed and identification ability. To this end, this paper collected and annotated a data set named "Aquarium Fish" of 84 fishes containing 10042 images, and based on this data set, proposed a multi-scale input fast fish target detection network (BTP-yoloV3) and its optimization method. The experiment uses Depthwise convolution to redesign the backbone of the yoloV4 network, which reduces the amount of calculation by 94.1%, and the test accuracy is 92.34%. Then, the training model is enhanced with MixUp, CutMix, and mosaic to increase the test accuracy by 1.27%; Finally, use the mish, swish, and ELU activation functions to increase the test accuracy by 0.76%. As a result, the accuracy of testing the network with 2000 fish images reached 94.37%, and the computational complexity of the network BFLOPS was only 5.47. Comparing the YoloV3~4, MobileNetV2-yoloV3, and YoloV3-tiny networks of migration learning on this data set. The results show that BTP-Yolov3 has smaller model parameters, faster calculation speed, and lower energy consumption during operation while ensuring the calculation accuracy. It provides a certain reference value for the practical application of neural network.

Related papers

• LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection [0.0]
  We focus on design choices of neural network architectures for efficient object detection based on FLOP. We propose several optimizations to enhance the efficiency of YOLO-based models. This paper contributes to a new scaling paradigm for object detection and YOLO-centric models called LeYOLO.
  arXiv  Detail & Related papers  (2024-06-20T12:08:24Z)
• Fast vehicle detection algorithm based on lightweight YOLO7-tiny [7.7600847187608135]
  This paper proposes a lightweight vehicle detection algorithm based on YOLOv7-tiny (You Only Look Once version seven) called Ghost-YOLOv7. The width of model is scaled to 0.5 and the standard convolution of the backbone network is replaced with Ghost convolution to achieve a lighter network and improve the detection speed. A Ghost Decouoled Head (GDH) is employed for accurate prediction of vehicle location and species.
  arXiv  Detail & Related papers  (2023-04-12T17:28:30Z)
• CNN-transformer mixed model for object detection [3.5897534810405403]
  In this paper, I propose a convolutional module with a transformer. It aims to improve the recognition accuracy of the model by fusing the detailed features extracted by CNN with the global features extracted by a transformer. After 100 rounds of training on the Pascal VOC dataset, the accuracy of the results reached 81%, which is 4.6 better than the faster RCNN[4] using resnet101[5] as the backbone.
  arXiv  Detail & Related papers  (2022-12-13T16:35:35Z)
• Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras [62.70541164894224]
  We introduce several architecture choices which allow us to scale the depth and complexity of such models while maintaining low computation. Our method runs 3.7 times faster than a dense graph neural network, taking only 8.4 ms per forward pass.
  arXiv  Detail & Related papers  (2022-11-22T15:14:20Z)
• Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios [8.721557548002737]
  This paper proposes a lightweight fire detection algorithm of Light-YOLOv5 that achieves a balance of speed and accuracy. Experiments show that Light-YOLOv5 improves mAP by 3.3% compared to the original algorithm, reduces the number of parameters by 27.1%, decreases the computation by 19.1%, achieves FPS of 91.1.
  arXiv  Detail & Related papers  (2022-08-29T08:36:04Z)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)
• PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices [13.62426382827205]
  PP-PicoDet family of real-time object detectors achieves superior performance on object detection for mobile devices. Models achieve better trade-offs between accuracy and latency compared to other popular models.
  arXiv  Detail & Related papers  (2021-11-01T12:53:17Z)
• FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation [81.76975488010213]
  Dense optical flow estimation plays a key role in many robotic vision tasks. Current networks often occupy large number of parameters and require heavy computation costs. Our proposed FastFlowNet works in the well-known coarse-to-fine manner with following innovations.
  arXiv  Detail & Related papers  (2021-03-08T03:09:37Z)
• Inception Convolution with Efficient Dilation Search [121.41030859447487]
  Dilation convolution is a critical mutant of standard convolution neural network to control effective receptive fields and handle large scale variance of objects. We propose a new mutant of dilated convolution, namely inception (dilated) convolution where the convolutions have independent dilation among different axes, channels and layers. We explore a practical method for fitting the complex inception convolution to the data, a simple while effective dilation search algorithm(EDO) based on statistical optimization is developed.
  arXiv  Detail & Related papers  (2020-12-25T14:58:35Z)
• 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)
• Filter Sketch for Network Pruning [184.41079868885265]
  We propose a novel network pruning approach by information preserving of pre-trained network weights (filters) Our approach, referred to as FilterSketch, encodes the second-order information of pre-trained weights. Experiments on CIFAR-10 show that FilterSketch reduces 63.3% of FLOPs and prunes 59.9% of network parameters with negligible accuracy cost.
  arXiv  Detail & Related papers  (2020-01-23T13:57:08Z)

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.