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Improved Faster R-CNN Algorithm for Sea Object Detection Under Complex Sea Conditions Cover

Improved Faster R-CNN Algorithm for Sea Object Detection Under Complex Sea Conditions

By:
Liu Yabin,  Yu Jun and  Hu Zhiyi  
Open Access
|Jul 2020

Figures & Tables

Figure 1.

Fast R-CNN model framework
Fast R-CNN model framework

Figure 2.

Region Proposal Networks
Region Proposal Networks

Figure 3.

mAP of different K values
mAP of different K values

Figure 4.

Is a schematic diagram of the shortcomings of The NMS algorithm
Is a schematic diagram of the shortcomings of The NMS algorithm

Figure 5.

Improved object detection network
Improved object detection network

Figure 6.

Test results
Test results

VALUES OF MAIN NETWORK PARAMETERS

ParametersValuesParametersValues
LEARNING_RATEle-3Bateh_size256
Anchor_Scales[8,16,32]Anchor_RATIOS[0.7, 1.2, 1.3, 2.9]
ITERS85000num_classes6
SOFT_NMS1

COMPARISON OF DETECTION RESULTS OF THREE NETWORK STRUCTURES

Detection methodPassenger_ shipCargo_shipContainer_ shipAircraft_ shipWar_shipmAP
VGG-16 structure50.9%80.6%92.4%98.1%92.8%82.96%
ResNet101 structure54.1%81.0%92.8%99.3%93.0%84.04%
improved ResNet101 structure66.9%82.3%93.65%99.51%93.9%87.25%

ANCHOR_RATIOS FOR DIFFERENT K VALUES

numberK=1K=2K=3K=4K=5K=6K=7
0.61.30.50.70.60.50.5
-1.41.21.21.20.70.6
--1.61.31.31.21.1
results--2.91.71.31.2
----2.62.41.8
-----32.1
------2.9
mAP(%)83.8484.384.3284.9884.2984.5784.34
Time(s)503510515518524515517
Language: English
Page range: 76 - 82
Published on: Jul 13, 2020
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2020 Liu Yabin, Yu Jun, Hu Zhiyi, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.