As we navigate our daily commutes, the threat posed by a distracted driver is
at a large, resulting in a troubling rise in traffic accidents. Addressing this
safety concern, our project harnesses the analytical power of Convolutional
Neural Networks (CNNs), with a particular emphasis on the well-established
models VGG16 and VGG19. These models are acclaimed for their precision in image
recognition and are meticulously tested for their ability to detect nuances in
driver behavior under varying environmental conditions. Through a comparative
analysis against an array of CNN architectures, this study seeks to identify
the most efficient model for real-time detection of driver distractions. The
ultimate aim is to incorporate the findings into vehicle safety systems,
significantly boosting their capability to prevent accidents triggered by
inattention. This research not only enhances our understanding of automotive
safety technologies but also marks a pivotal step towards creating vehicles
that are intuitively aligned with driver behaviors, ensuring safer roads for
all.

通过比较分析卷积神经网络结构，本研究旨在识别出最有效的实时检测驾驶员分心的模型，以提升车辆安全系统的能力，从而预防因不注意引发的事故。

通过卷积神经网络实时检测驾驶员注意力分散以增强道路安全

Enhancing Road Safety: Real-Time Detection of Driver Distraction through  Convolutional Neural Networks

Diabetic retinopathy (DR) is a leading cause of blindness among diabetic
patients. Deep learning models have shown promising results in automating the
detection of DR. In the present work, we propose a new methodology that
integrates a feature attention module with a pretrained VGG19 convolutional
neural network (CNN) for more accurate DR detection. Here, the pretrained net
is fine-tuned with the proposed feature attention block. The proposed module
aims to leverage the complementary information from various regions of fundus
images to enhance the discriminative power of the CNN. The said feature
attention module incorporates an attention mechanism which selectively
highlights salient features from images and fuses them with the original input.
The simultaneous learning of attention weights for the features and thereupon
the combination of attention-modulated features within the feature attention
block facilitates the network's ability to focus on relevant information while
reducing the impact of noisy or irrelevant features. Performance of the
proposed method has been evaluated on a widely used dataset for diabetic
retinopathy classification e.g., the APTOS (Asia Pacific Tele-Ophthalmology
Society) DR Dataset. Results are compared with/without attention module, as
well as with other state-of-the-art approaches. Results confirm that the
introduction of the fusion module (fusing of feature attention module with CNN)
improves the accuracy of DR detection achieving an accuracy of 95.70%.

我们提出了一种新的方法，将特征注意模块与预训练的 VGG19 卷积神经网络（CNN）集成在一起，用于更准确地检测糖尿病视网膜病变（DR）。实验结果表明，融合模块（将特征注意模块与 CNN 融合）的引入改善了 DR 检测的准确性，实现了 95.70% 的准确率。