ISA: Ingenious Siamese Attention for object detection algorithms towards complex scenes

The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-ba...

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Vydané v:ISA transactions Ročník 143; s. 205 - 220
Hlavní autori: Liu, Lianjun, Hu, Ziyu, Dai, Yan, Ma, Xuemin, Deng, Pengwei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.12.2023
Predmet:
Siamese network
YOLO
Complex scenes
Object detection
ISSN:0019-0578, 1879-2022, 1879-2022
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Shrnutí:The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-based tasks in complex environments. In order to ameliorate the detection accuracy of object detection algorithms under various complex environment transformations, this work proposes the Siamese Attention YOLO (SAYOLO) object detection algorithm based on ingenious siamese attention structure. The ingenious siamese attention structure includes three aspects: Attention Neck YOLOv4 (ANYOLOv4), siamese neural network structure and special designed network scoring module. In the Complex Mini VOC dataset, the detection accuracy of SAYOLO algorithm is 12.31%, 48.93%, 17.80%, 10.12%, 18.79% and 1.12% higher than Faster-RCNN (Resnet50), SSD (Mobilenetv2), YOLOv3, YOLOv4, YOLOv5-l and YOLOX-x, respectively. Compared with traditional object detection algorithms based on image preprocessing, the detection accuracy of SAYOLO is 4.88%, 11.51%, 1.73%, 23.27%, 18.12%, and 5.76% higher than Image-Adaptive YOLO, MSBDN-DFF + YOLOv4, Dark Channel Prior + YOLOv4, Zero-DCE + YOLOv4, MSBDN-DFF + Zero-DCE + YOLOv4, and Dark Channel Prior + Zero-DCE + YOLOv4, respectively.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0019-0578
1879-2022
1879-2022
DOI:10.1016/j.isatra.2023.09.001