DAAM-YOLOV5: A Helmet Detection Algorithm Combined with Dynamic Anchor Box and Attention Mechanism

Abstract

Helmet recognition algorithms based on deep learning aim to enable unmanned full-time detection and record violations such as failure to wear a helmet. However, in actual scenarios, weather and human factors can be complicated, which poses challenges for safety helmet detection. Camera shaking and head occlusion are common issues that can lead to inaccurate results and low availability. To address these practical problems, this paper proposes a novel helmet detection algorithm called DAAM-YOLOv5. The DAAM-YOLOv5 algorithm enriches the diversity of datasets under different weather conditions to improve the mAP of the model in corresponding scenarios by using Mosaic-9 data enhancement. Additionally, this paper introduces a novel dynamic anchor box mechanism, K-DAFS, into this algorithm and enhances the generation speed of the blocked target anchor boxes by using bidirectional feature fusion (BFF). Furthermore, by using an attention mechanism, this paper redistributes the weight of objects in a picture and appropriately reduces the model’s sensitivity to the edge information of occluded objects through pooling. This approach improves the model’s generalization ability, which aligns with practical application requirements. To evaluate the proposed algorithm, this paper adopts the region of interest (ROI) detection strategy and carries out experiments on specific, real datasets. Compared with traditional deep learning algorithms on the same datasets, our method effectively distinguishes helmet-wearing conditions even when head information is occluded and improves the detection speed of the model. Moreover, compared with the YOLOv5s algorithm, the proposed algorithm increases the mAP and FPS by 4.32% and 9 frames/s, respectively.

Capsule Defect Detection Method Based on an Improved Model

“…The adaptive anchor box calculation [10] sets a fixed anchor box size for the dataset. This enhancement method enriches the dataset, significantly improves the training speed of the network and reduces the memory requirements of the model [11]. Adaptive image scaling is used in the model inference process to avoid information redundancy and speed up the inference process.…”

FEFD-YOLOV5: A Helmet Detection Algorithm Combined with Feature Enhancement and Feature Denoising

“…By introducing feature pyramids and multi-scale perception modules, the robustness of the detection algorithm to target scale changes was improved. Weipeng Tai et al [24] enhanced the features of the spatial region corresponding to the target object by introducing a spatial attention module, thereby improving helmet recognition rate.…”

YOLO-LHD: an enhanced lightweight approach for helmet wearing detection in industrial environments

“…Zhou et al (2021) used the YOLOv5 model for safety helmet detection, demonstrating the effectiveness of helmet detection based on YOLOv5. Tai et al (2023) improved the YOLOv5 algorithm using attention mechanisms and dynamic anchor boxes, enhancing the detection accuracy and speed for occluded targets.…”