Abstract
In the practical application scenarios of safety helmet detection, the lightweight algorithm You Only Look Once (YOLO) v3-tiny is easy to be deployed in embedded devices because its number of parameters is small. However, its detection accuracy is relatively low, which is why it is not suitable for detecting multi-scale safety helmets. The safety helmet detection algorithm (named SAS-YOLOv3-tiny) is proposed in this paper to balance detection accuracy and model complexity. A light Sandglass-Residual (SR) module based on depthwise separable convolution and channel attention mechanism is constructed to replace the original convolution layer, and the convolution layer of stride two is used to replace the max-pooling layer for obtaining more informative features and promoting detection performance while reducing the number of parameters and computation. Instead of two-scale feature prediction, three-scale feature prediction is used here to improve the detection effect about small objects further. In addition, an improved spatial pyramid pooling (SPP) module is added to the feature extraction network to extract local and global features with rich semantic information. Complete-Intersection over Union (CIoU) loss is also introduced in this paper to improve the loss function for promoting positioning accuracy. The results on the self-built helmet dataset show that the improved algorithm is superior to the original algorithm. Compared with the original YOLOv3-tiny, the SAS-YOLOv3-tiny has significantly improved all metrics (including Precision (P), Recall (R), Mean Average Precision (mAP), F1) at the expense of only a minor speed while keeping fewer parameters and amounts of calculation. Meanwhile, the SAS-YOLOv3-tiny algorithm shows advantages in accuracy compared with lightweight object detection algorithms, and its speed is faster than the heavyweight model.
Highlights
Driving a motorcycle or an electric two-wheeler without a safety helmet will cause a high mortality rate
One part of the processed results is processed through the convolutions and used to output predictions in terms of the current feature map, and the other part is processed through a convolution layer and an up-sampling operations, and is fused with the corresponding upper more detailed and lower-level location information, which can obtain feature map containing both semantic and positional information
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Summary
Driving a motorcycle or an electric two-wheeler without a safety helmet will cause a high mortality rate. Object classification and bounding box regression can be optimized end-to-end without requiring additional cache space while it had better detection accuracy and faster reasoning speed than R-CNN and SPP-Net. Even though model learning has improved, the generation of the proposal still relied on traditional methods. Even though YOLOv3 is a widely used object detection algorithm with good recognition speed and detection accuracy by combining several methods such as residual network, feature pyramid and multi-feature fusion network, it has lots of parameters and amount of computation and generates a large model. The three-scale feature prediction method is introduced into the network structure of SAS-YOLOv3-tiny to improve the two-scale feature prediction for obtaining accurate location information of small objects further.
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