YOLO-SGF:基于改进型 YOLOv8 的用于复杂红外图像中物体检测的轻量级网络

IF 3.1 3区 物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION Infrared Physics & Technology Pub Date : 2024-08-31 DOI:10.1016/j.infrared.2024.105539
Cong Guo, Kan Ren, Qian Chen
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引用次数: 0

摘要

目前主流的物体检测网络在 RGB 可见光图像中表现出色,但应用于低分辨率红外图像时需要大量计算资源且性能下降。针对上述问题,我们提出了一种基于只看一次版本8(YOLOv8)的轻量级算法YOLO-SGF。首先,设计了轻量级跨尺度特征图融合网络 GCFVoV,以解决检测精度低的问题,并保持轻量级网络的低复杂度。而 GCFVoV neck 中的轻量级 GCVF 模块使用 GSConv 和 Conv 分别处理深层和浅层特征,最大限度地保留了各通道之间的隐含联系,整合了多尺度特征。其次,我们利用 ShuffleNetV2-block1 与 C2f 结合进行特征提取,使算法更加轻便有效。最后,我们提出了 FIMPDIoU 损失函数,该函数关注复杂背景中被忽略的物体,并根据不同大小的物体使用特定的比率调整预测框。在红外数据集中,与 YOLOv8 相比,YOLO-SGF 的计算空间复杂度降低了 50%,时间复杂度降低了 42%,FPS32 提高了 36.3%,物体检测的 [email protected] ∼ 0.95 提高了 1.1%。我们的算法增强了红外图像中的物体检测能力,尤其是在夜间、弱光和遮挡条件下。YOLO-SGF 可以部署在计算能力有限的嵌入式边缘设备上,为轻量级网络提供了新思路。
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YOLO-SGF: Lightweight network for object detection in complex infrared images based on improved YOLOv8

The current mainstream object detection networks perform well in RGB visible images, but they require high computational resource and degrade in performance when applied to low-resolution infrared images. To address above issues, we propose a lightweight algorithm YOLO-SGF based on you-only-look-once version8 (YOLOv8). Firstly, the lightweight cross-scale feature map fusion network GCFVoV designed as neck to solve poor detection accuracy and maintain low complexity in lightweight networks. And a lightweight GCVF module in GCFVoV neck uses GSConv and Conv to process deep and shallow features respectively, which maximally preserves implicit connections between each channel and integrates multi-scale features. Secondly, we utilize ShuffleNetV2-block1 in combination with C2f for feature extraction, making the algorithm more lightweight and effectively. Finally, we propose the FIMPDIoU loss function, which focuses on overlooked objects in complex backgrounds and adjusts the prediction boxes using ratios specific to different sizes of objects. Compared with YOLOv8 in our infrared dataset, YOLO-SGF reduces the computational space complexity by 50 % and time complexity by 42 %, increases FPS32 by 36.3 % and improves [email protected] ∼ 0.95 by 1.1 % in object detection. Our algorithm enhances the capability of object detection in infrared images especially in nighttime, low light, and occluded conditions. YOLO-SGF enables deployment on embedded edge devices with limited computing power, and provides a new idea for lightweight networks.

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来源期刊
CiteScore
5.70
自引率
12.10%
发文量
400
审稿时长
67 days
期刊介绍: The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region. Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine. Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.
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