Detecting Personal Protective Equipment in images and video streams is a
relevant problem in ensuring the safety of construction workers. In this
contribution, an architecture enabling live image recognition of such equipment
is proposed. The solution is deployable in two settings -- edge-cloud and
edge-only. The system was tested on an active construction site, as a part of a
larger scenario, within the scope of the ASSIST-IoT H2020 project. To determine
the feasibility of the edge-only variant, a model for counting people wearing
safety helmets was developed using the YOLOX method. It was found that an
edge-only deployment is possible for this use case, given the hardware
infrastructure available on site. In the preliminary evaluation, several
important observations were made, that are crucial to the further development
and deployment of the system. Future work will include an in-depth
investigation of performance aspects of the two architecture variants.

本文提出了一种在线识别人们佩戴个人防护装备的架构，可用于确保建筑工人的安全，可以在边缘 - 云和仅边缘的两种设置下部署，使用 YOLOX 方法在建筑工地上进行了测试，初步评估表明，对于这种用例，边缘部署是可行的，未来的工作将包括对两种架构变体性能方面进行深入研究和部署。

面向个人防护装备检测的边缘云架构

Towards Edge-Cloud Architectures for Personal Protective Equipment  Detection

In this report, we introduce our real-time 2D object detection system for the
realistic autonomous driving scenario. Our detector is built on a newly
designed YOLO model, called YOLOX. On the Argoverse-HD dataset, our system
achieves 41.0 streaming AP, which surpassed second place by 7.8/6.1 on
detection-only track/fully track, respectively. Moreover, equipped with
TensorRT, our model achieves the 30FPS inference speed with a high-resolution
input size (e.g., 1440-2304). Code and models will be available at
this https URL

本文介绍我们基于 YOLOX 模型设计的实时 2D 目标检测系统，适用于现实中自主驾驶场景，我们在 Argoverse-HD 数据集上实现了 41.0 的实时准确率，超过了第二名 7.8/6.1 的检测轨迹 / 完全轨迹，此外，配备 TensorRT，我们的模型在高分辨率输入大小下达到了 30FPS 的推理速度。

CVPR 2021 自动驾驶研讨会：流式感知挑战技术报告

Workshop on Autonomous Driving at CVPR 2021: Technical Report for  Streaming Perception Challenge

In this report, we present some experienced improvements to YOLO series,
forming a new high-performance detector -- YOLOX. We switch the YOLO detector
to an anchor-free manner and conduct other advanced detection techniques, i.e.,
a decoupled head and the leading label assignment strategy SimOTA to achieve
state-of-the-art results across a large scale range of models: For YOLO-Nano
with only 0.91M parameters and 1.08G FLOPs, we get 25.3% AP on COCO, surpassing
NanoDet by 1.8% AP; for YOLOv3, one of the most widely used detectors in
industry, we boost it to 47.3% AP on COCO, outperforming the current best
practice by 3.0% AP; for YOLOX-L with roughly the same amount of parameters as
YOLOv4-CSP, YOLOv5-L, we achieve 50.0% AP on COCO at a speed of 68.9 FPS on
Tesla V100, exceeding YOLOv5-L by 1.8% AP. Further, we won the 1st Place on
Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021)
using a single YOLOX-L model. We hope this report can provide useful experience
for developers and researchers in practical scenes, and we also provide deploy
versions with ONNX, TensorRT, NCNN, and Openvino supported. Source code is at
this https URL

本文介绍了基于 YOLO 系列的改进，从而形成了一种新的高性能检测器 ——YOLOX，采用无 anchor 的方式及其他先进的检测技术，达到了业界领先水平，并在特定领域应用中获得了第一名的成绩。