Remote Sensing Image Aircraft Target Detection Based on GIoU-YOLO v3
Yumin Yang, Yurong Liao, Lingfeng Cheng, Ke Zhang, Haining Wang, Shimiao Chen
Abstract
Based on the YOLO v3 target detection framework, this paper trains and learns the public remote sensing image aircraft target data, and optimizes the defects of the YOLO v3 loss function, and introduces the IoU (intersection ratio) between the ground-true box and the prediction box, experimental results show that the precision, recall ratio and F1 value of the YOLO v3 model for aircraft target detection in remote sensing images are 95.12%, 86.21% and 0.9045, respectively. Compared with the previous ones, the network precision, recall rate and F1 value of the optimized loss function have been improved by 12.57%, 5.11% and 0.0863 respectively.
Topics & Concepts
Intersection (aeronautics)Computer scienceArtificial intelligenceImage (mathematics)Computer visionFunction (biology)Remote sensingObject detectionRecall ratePrecision and recallPattern recognition (psychology)EngineeringGeographyBiologyAerospace engineeringEvolutionary biologyAdvanced Neural Network ApplicationsInfrared Target Detection MethodologiesIndustrial Vision Systems and Defect Detection