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Multilevel Fine-Grained Features-Based General Framework for Object Detection
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AI
DOI：10.1109/TCYB.2024.3424430.png)
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摘要
En 中文
This article proposes a practical and generalizable object detector, termed feature extraction-fusion-prediction network (FEFP-Net) for real-world application scenarios. The existing object detection methods have recently achieved excellent performance, however they still face three major challenges for real-world applications, i.e., feature similarity between classes, object size variability, and inconsistent localization and classification predictions. In order to effectively alleviate the current difficulties, the FEFP-Net with three key components is proposed, and the improved detection accuracy is proved in various applications: 1) Extraction Phase: an adaptive fine-grained feature extraction network is proposed to capture features of interest from coarse to fine details, which effectively avoids misclassification due to feature similarity; 2) Fusion Phase: a bidirectional neighbor connection network is designed to identify objects with different sizes by aggregating multilevel features and 3) Prediction Phase: in order to improve the accuracy of object localization and classification, a task specific prediction network is presented, which sufficiently exploits both the spatial and channel information of features. Compared with the State-of-the-Art methods, we achieved competitive results in the MS-COCO dataset. Further, we demonstrated the performance of FEFP-Net in different application fields, such as medical imaging, industry, agriculture, transportation, and remote sensing. These comprehensive experiments indicate that FEFP-Net has satisfactory accuracy and generalizability as a basic object detector.
Keyword:
Feature extraction
Task analysis
Detectors
Object detection
Accuracy
Semantics
Location awareness
Multilevel fine-grained features
object detector
real-world applications
task specific prediction network (TSPN)
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
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