Accurate identification of vehicles and estimating density in traffic surveillance systems is a challenging task, particularly in scenarios with closely spaced lanes. Single Shot MultiBox Detector (SSD) is introduced in vehicle detection and classification due to its speed and accuracy. It utilizes a transfer learning technique that enables them to utilize features from pretrained Convolutional Neural Networks (CNNs). Although the utilization of multi-scale feature maps in SSD has achieved efficient results in vehicle detection, it struggles to identify key features of vehicles due to its one-stage detection approach. Furthermore, the use of VGG16 as the backbone network in these approaches leads to the loss of fine-grained details, posing challenges in accurately localizing and classifying small vehicles. Also, there is no interaction between high- and low-level features, which restricts the networks ability to effectively integrate and utilize both features for accurate vehicle detection. To overcome these limitations, an improved SSD approach is introduced in this paper, leveraging interactive multi-scale attention characteristics to accurately detect and classify vehicles. This approach utilizes ResNet50 as the network backbone to overcome the limitations of traditional SSDs in detecting small vehicles. Also, an attention block is incorporated into it to focus on key details and assign higher attention to relevant pixels within the feature map. Also, the network employs a parallel detection framework and shares multi-scale layers (both high and low), enabling efficient detection of vehicles with various sizes. Then, traffic density is estimated based on the weights assigned to the categorized vehicles obtained from the improved SSD and the recorded area. Finally, traffic is classified as high, low, or moderate by comparing the estimated density to a threshold value. By adding attention characteristics of different scales to the original detection branch and replacing the VGG16 with ResNet50 of the SSD technique using our method, the feature representation capability and detection accuracy are both significantly improved.

在交通监控系统中准确识别车辆并估计密度是一项具有挑战性的任务，特别是在车道间隔很近的情况下。Single Shot MultiBox Detector (SSD) 因其速度和准确性而被引入车辆检测和分类中。它利用迁移学习技术，使他们能够利用预训练的卷积神经网络 (CNN) 的特征。尽管SSD中多尺度特征图的利用在车辆检测中取得了有效的结果，但由于其单阶段检测方法，很难识别车辆的关键特征。此外，在这些方法中使用 VGG16 作为骨干网络会导致细粒度细节的丢失，给小型车辆的准确定位和分类带来挑战。此外，高级和低级特征之间没有交互，这限制了网络有效集成和利用这两种特征进行准确车辆检测的能力。为了克服这些限制，本文引入了一种改进的 SSD 方法，利用交互式多尺度注意力特征来准确检测和分类车辆。该方法利用ResNet50作为网络主干，克服了传统SSD在检测小型车辆方面的局限性。此外，其中还包含一个注意力块，以关注关键细节并对特征图中的相关像素分配更高的注意力。此外，该网络采用并行检测框架并共享多尺度层（高层和低层），能够有效检测各种尺寸的车辆。然后，根据从改进的SSD和记录区域获得的分配给分类车辆的权重来估计交通密度。最后，通过将估计的密度与阈值进行比较，将流量分类为高、低或中等。通过在原始检测分支中添加不同尺度的注意力特征，并使用SSD技术的ResNet50替换VGG16，特征表示能力和检测精度均得到显着提高。