Liu Ziyuan, Sun Chunxia, Wang Xiaopeng
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel). 2024 Jul 17;24(14):4628. doi: 10.3390/s24144628.
In foggy weather, outdoor safety helmet detection often suffers from low visibility and unclear objects, hindering optimal detector performance. Moreover, safety helmets typically appear as small objects at construction sites, prone to occlusion and difficult to distinguish from complex backgrounds, further exacerbating the detection challenge. Therefore, the real-time and precise detection of safety helmet usage among construction personnel, particularly in adverse weather conditions such as foggy weather, poses a significant challenge. To address this issue, this paper proposes the DST-DETR, a framework for foggy weather safety helmet detection. The DST-DETR framework comprises a dehazing module, PAOD-Net, and an object detection module, ST-DETR, for joint dehazing and detection. Initially, foggy images are restored within PAOD-Net, enhancing the AOD-Net model by introducing a novel convolutional module, PfConv, guided by the parameter-free average attention module (PfAAM). This module enables more focused attention on crucial features in lightweight models, therefore enhancing performance. Subsequently, the MS-SSIM + ℓ2 loss function is employed to bolster the model's robustness, making it adaptable to scenes with intricate backgrounds and variable fog densities. Next, within the object detection module, the ST-DETR model is designed to address small objects. By refining the RT-DETR model, its capability to detect small objects in low-quality images is enhanced. The core of this approach lies in utilizing the variant ResNet-18 as the backbone to make the network lightweight without sacrificing accuracy, followed by effectively integrating the small-object layer into the improved BiFPN neck structure, resulting in CCFF-BiFPN-P2. Various experiments were conducted to qualitatively and quantitatively compare our method with several state-of-the-art approaches, demonstrating its superiority. The results validate that the DST-DETR algorithm is better suited for foggy safety helmet detection tasks in construction scenarios.
在雾天，户外安全帽检测常常因能见度低和物体不清晰而受到影响，阻碍了探测器的最佳性能。此外，安全帽在建筑工地通常呈现为小物体，容易被遮挡，且难以从复杂背景中区分出来，这进一步加剧了检测挑战。因此，实时、精确地检测建筑人员是否佩戴安全帽，尤其是在雾天等恶劣天气条件下，是一项重大挑战。为解决这一问题，本文提出了DST-DETR，一种用于雾天安全帽检测的框架。DST-DETR框架由一个去雾模块、PAOD-Net和一个目标检测模块ST-DETR组成，用于联合去雾和检测。首先，在PAOD-Net中对雾天图像进行恢复，通过引入一种新颖的卷积模块PfConv对AOD-Net模型进行增强，该模块由无参数平均注意力模块（PfAAM）引导。该模块能够在轻量级模型中更聚焦于关键特征，从而提高性能。随后，采用MS-SSIM + ℓ2损失函数来增强模型的鲁棒性，使其适用于具有复杂背景和可变雾密度的场景。接下来，在目标检测模块中，设计ST-DETR模型来处理小物体。通过对RT-DETR模型进行改进，增强了其在低质量图像中检测小物体的能力。该方法的核心在于利用变体ResNet-18作为骨干网络，在不牺牲准确性的情况下使网络轻量化，随后将小物体层有效地集成到改进的BiFPN颈部结构中，得到CCFF-BiFPN-P2。进行了各种实验，对我们的方法与几种先进方法进行了定性和定量比较，证明了其优越性。结果验证了DST-DETR算法更适合建筑场景中的雾天安全帽检测任务。
