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Huixin Wu1, Yang Zhu2, Shuqi Li3

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The CDYL object detection algorithm improves infrared and visible light image analysis by enhancing sensitivity to small objects. This novel approach, utilizing a Convolution to Fully Connected-Deformable Convolution module, boosts detection accuracy for challenging datasets.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Small and difficult-to-detect objects pose challenges in infrared and visible light imaging.
  • Traditional Convolutional Neural Networks (CNNs) have limitations in capturing long-range dependencies and adaptive spatial information.

Purpose of the Study:

  • To develop an object detection algorithm, CDYL (Convolution to Fully Connected-Deformable Convolution You only Look once), for improved detection of small objects.
  • To enhance the model's ability to process infrared and visible light images by incorporating adaptive spatial aggregation and reducing inductive bias.

Main Methods:

  • Proposed the core Convolution to Fully Connected-Deformable Convolution (CFC-DC) module for CDYL.
  • Integrated the Mish activation function to improve generalization and perception of image details.
  • Evaluated CDYL on infrared and UAV image datasets, comparing it against state-of-the-art algorithms like YOLOv8l.

Main Results:

  • CDYL achieved a 3.0% improvement in mAP0.5 for infrared image detection compared to YOLOv8l.
  • Demonstrated a 1.1% improvement in mAP0.5 for visible light image detection tasks.
  • The algorithm showed superior average precision on both infrared and visible light images while maintaining a lightweight architecture.

Conclusions:

  • CDYL effectively addresses the challenge of detecting small objects in infrared and visible light imagery.
  • The CFC-DC module enhances receptive field and spatial aggregation, leading to improved detection performance.
  • The proposed algorithm offers a lightweight and generalized solution for dense small object detection.