The engineering vehicle detection is a key issue for the raw material warehouse scenes. Through the engineering vehicle detection, the working conditions of engineering vehicles in the raw material warehouse can be intelligently managed to prevent large-scale smoke pollution and the danger of smoke and dust. In this paper, we propose an intelligent method based on the framework of YOLOv4-Tiny for locating and identifying the engineering vehicles. In our detection task, the monitoring scenes are complex with a lot of interference. And the scope of monitoring is large. In order to solve these challenging problems, we introduce the Split-attention module to the network, which can adaptively extract important information of the image and improve the receptive field of detection. In addition, we introduce the Dynamic ReLU function to the network, which allow the network to adaptively learn more suitable ReLU parameters based on the input. We also collect a large number of images obtained from the front-end cameras and create a self-built dataset of engineering vehicles. In this paper, we test our method on the COCO dataset and the self-built engineering vehicle dataset. Experimental results show that our method proposed in this paper can detect engineering vehicles with higher accuracy and faster speed, which can be used for engineering vehicle detection in the scenes of raw material storage warehouses.

工程车辆检测是原材料仓库场景的关键问题。通过工程车辆检测，可以对原材料仓库内工程车辆的工况进行智能管理，杜绝大面积烟尘污染和烟尘危害。在本文中，我们提出了一种基于 YOLOv4-Tiny 框架的智能工程车辆定位识别方法。在我们的检测任务中，监控场景复杂，干扰很多。而且监测范围大。为了解决这些具有挑战性的问题，我们在网络中引入了Split-attention模块，可以自适应地提取图像的重要信息，提高检测的感受野。此外，我们在网络中引入了 Dynamic ReLU 功能，这允许网络根据输入自适应地学习更合适的 ReLU 参数。我们还收集了大量从前端摄像头获取的图像，并创建了一个自建的工程车辆数据集。在本文中，我们在 COCO 数据集和自建工程车辆数据集上测试了我们的方法。实验结果表明，本文提出的方法能够以更高的精度和更快的速度检测工程车辆，可用于原材料仓库场景下的工程车辆检测。我们在 COCO 数据集和自建工程车辆数据集上测试了我们的方法。实验结果表明，本文提出的方法能够以更高的精度和更快的速度检测工程车辆，可用于原材料仓库场景下的工程车辆检测。我们在 COCO 数据集和自建工程车辆数据集上测试了我们的方法。实验结果表明，本文提出的方法能够以更高的精度和更快的速度检测工程车辆，可用于原材料仓库场景下的工程车辆检测。