title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð´Ð»Ñ Ð´ÐµÑÐµÐºÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ¾Ð¼Ð¿ÑÐµÑÑÐ¸Ð¸ Ð¸ ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Research on model optimization methods for object detection using neural network compression and acceleration techniques
Creators: name: ÐÐ²Ð°Ð½Ð¾Ð², ÐÐµÐ½Ð¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2025
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ
ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ðµ Ð¸Ð½ÑÐµÑÐµÐ½ÑÐ°
yolo
sar-ÑÐ½Ð¸Ð¼ÐºÐ¸
tensorrt
pytorch
neural networks
optimization
inference acceleration
sar images

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÑ Ð´Ð»Ñ Ð·Ð°Ð´Ð°Ñ Ð´ÐµÑÐµÐºÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ (SAR-ÑÐ½Ð¸Ð¼ÐºÐ°Ñ). ÐÑÐ½Ð¾Ð²Ð½Ð¾Ðµ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ðµ ÑÐ´ÐµÐ»ÑÐµÑÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ YOLO, ÐºÐ¾ÑÐ¾ÑÐ°Ñ ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð´Ð½Ð¾Ð¹ Ð¸Ð· ÑÐ°Ð¼ÑÑ Ð±ÑÑÑÑÑÑ Ð¸ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ Ð´Ð»Ñ Ð´ÐµÑÐµÐºÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð² ÑÐµÐ°Ð»ÑÐ½Ð¾Ð¼ Ð²ÑÐµÐ¼ÐµÐ½Ð¸. Ð ÑÐ°Ð¼ÐºÐ°Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ Ð¼ÐµÑÐ¾Ð´Ñ, ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº ÐºÐ²Ð°Ð½ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¸ Ð¿ÑÑÐ½Ð¸Ð½Ð³, Ð´Ð»Ñ ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ñ Ð¸Ð½ÑÐµÑÐµÐ½ÑÐ° Ð¿ÑÐ¸ ÑÐ¾ÑÑÐ°Ð½ÐµÐ½Ð¸Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° Ð½Ð°ÑÑÑÐ¾Ð¹ÐºÐ° Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ YOLO Ð½Ð° Ð´Ð°ÑÐ°ÑÐµÑÐµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ ÑÐ°Ð½ÐºÐµÑÐ¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÑ Ñ Ð¸Ð½ÑÐµÑÐµÐ½ÑÐ¾Ð¼ Ð² ÑÑÐµÐ¹Ð¼Ð²Ð¾ÑÐºÐ°Ñ PyTorch Ð¸ TensorRT. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¸ Ð·Ð½Ð°ÑÐ¸ÑÐµÐ»ÑÐ½ÑÐµ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð² ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¸Ð½ÑÐµÑÐµÐ½ÑÐ° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ TensorRT Ð¿ÑÐ¸ Ð½ÐµÐ·Ð½Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¼ ÑÐ½Ð¸Ð¶ÐµÐ½Ð¸Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸. Ð Ð°Ð±Ð¾ÑÐ° Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð° Ð½Ð° Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð´ÐµÑÐµÐºÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° SAR-ÑÐ½Ð¸Ð¼ÐºÐ°Ñ, ÑÑÐ¾ Ð¸Ð¼ÐµÐµÑ Ð²Ð°Ð¶Ð½Ð¾Ðµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ðµ Ð´Ð»Ñ Ð·Ð°Ð´Ð°Ñ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° Ð¾ÐºÑÑÐ¶Ð°ÑÑÐµÐ¹ ÑÑÐµÐ´Ñ, Ð² ÑÐ¾Ð¼ ÑÐ¸ÑÐ»Ðµ Ð´Ð»Ñ Ð¼Ð¾ÑÑÐºÐ¾Ð¹ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÐ¸ Ð¸ Ð·Ð°ÑÐ¸ÑÑ Ð¾Ñ Ð¿ÑÐ¸ÑÐ¾Ð´Ð½ÑÑ ÐºÐ°ÑÐ°ÑÑÑÐ¾Ñ.
description: This work is focused on the research of neural network acceleration methods applied to object detection tasks on synthetic aperture radar (SAR) images. The main attention is paid to the optimization of the YOLO model, which is one of the fastest and most efficient for real-time object detection. The research explores techniques such as quantization and pruning to speed up inference while maintaining model accuracy. During the work, the YOLO model was trained on a tanker image dataset, and experiments were conducted comparing inference performance in the PyTorch and TensorRT frameworks. The results demonstrated significant improvements in inference speed using TensorRT with minimal accuracy loss. The work aims to enhance the efficiency of object detection on SAR images, which is crucial for environmental monitoring tasks, including marine security and disaster prevention.