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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Embedded Systems

Background:

  • Advancements in AI and image processing enable new applications.
  • Implementing computationally intensive algorithms on lightweight embedded boards is challenging.
  • Existing methods struggle with real-time processing on resource-constrained devices.

Purpose of the Study:

  • To propose a real-time deep learning object recognition method for lightweight embedded boards.
  • To develop an algorithm balancing computational load and accuracy for embedded systems.
  • To enhance object detection performance in resource-limited environments.

Main Methods:

  • Utilized two deep neural network architectures: one for low-computation semantic segmentation (Efficient Neural Network - ENet) and another for high-accuracy object detection (You Only Look Once - YOLO).
  • Implemented semantic segmentation to mask relevant areas, followed by object detection on the filtered image.
  • Employed OpenCV in Python for image processing and integrated ENet and YOLO for efficient real-time analysis.

Main Results:

  • Achieved a significant reduction in average error by approximately 2.4 times compared to baseline methods.
  • Enabled real-time object recognition on lightweight embedded boards at approximately 4 frames per second (FPS).
  • Improved object detection accuracy by filtering images through semantic segmentation, reducing background interference.

Conclusions:

  • The proposed method effectively enables accurate, real-time object recognition on lightweight embedded boards.
  • The dual-architecture approach successfully balances computational efficiency and detection accuracy.
  • This technique offers a viable solution for deploying advanced AI vision tasks on resource-constrained devices.