一个改进的新YOLOv7算法用于从街景图像中检测建筑空调外部单元
Zhongmin Tian1, Fei Yang2,3, Donghong Qin1
1College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
一个新的算法SC4-YOLOv7,从街景图像准确地检测城市空调. 这一进步有助于评估城市的耐热性,并为防灾战略提供信息.
科学领域:
- 城市环境监测 城市环境监测
- 计算机视觉应用程序的应用程序
- 遥感技术 遥感技术
背景情况:
- 街景图像提供有价值的城市环境数据.
- 准确的空调检测对于热浪弹性和灾害政策至关重要.
- 在复杂的街道场景中检测小型空调单元是一项挑战.
研究的目的:
- 开发一个改进的算法来检测和识别街景图像中的城市空调外部单元.
- 在复杂的视觉环境中提高小目标检测的准确性和效率.
- 为城市规划提供一种可靠的方法来量化空调覆盖范围.
主要方法:
- 将3D无参数注意力和协调注意力模块集成到目标检测中.
- 包括一个小的目标检测层来解决错过的检测.
- 使用来自吉林市的街景图像开发和验证SC4-YOLOv7算法.
主要成果:
- 与原来的YOLOv7.7相比,SC4-YOLOv7算法显著提高了空调识别的平均准确性,从87.93%提高到91.21%.
- 增强的算法保持了高速的图像识别检测.
- 在检测小型和容易错过的空调单元方面表现出有效性.
结论:
- SC4-YOLOv7为在街景图像中检测空调外部单元提供了更准确,更有效的解决方案.
- 该算法显示了在现实街道环境中的小目标检测中更广泛应用的潜力.
- 这项技术可以支持城市弹性评估和防灾工作.
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