The rapidly developing autonomous driving field now needs a more secure transportation system through information between multiple mobility. Deep learning that can judge traffic conditions by convergence of various sensor data and in particular, research on the convolutional neural network using computer vision are being actively conducted. In addition, recognizing many objects at once in a large area through drone images and understanding the movement of the object is used as safe traffic assistance information. In this study, an image classification study is conducted to determine the status of the vehicle on the road through drone flight image data. The goal is to build a new image classification model robust to the proposed image classification network by applying the weighted adversarial learning method. Weight adversarial learning is a method of securing robust performance in image classification of various statuses while disturbing the model by forcibly reflecting the slope value in reverse when updating the network through the reverse gradient layer. In the experiment, model performance is evaluated through the collected drone flight data set.

Object detection is an important study in computer vision to discriminate the position and class of an object in an image. Object detection in drone images is a technology that automatically detects and classifies objects using deep learning algorithms in flight images taken by drones. Object detection using drone images can rescue human life in disaster situations, grasp the situation at the disaster site, and identify the growth status of crops or pests in agriculture. In addition, it can be used in various fields such as infrastructure management, roads and railways, and city planning. A quick calculation is required. Although rapid computation is possible due to recent hardware development, there are many difficulties in using GPUs in industrial settings. In order to utilize drones in industrial sites, an object detection algorithm capable of real-time operation in a low-cost device is required. In this paper, we propose YOLOv5 with the combination of Coordinate Attention and CBAM for Object Detection on Drone for an algorithm capable of real-time operation in a low-cost device. The proposed architecture makes the model lighter by reducing the number of parameters and improves the object detection rate of the model through Coordinate Attention and CBAM. The model is trained using the VisDrone dataset, and the object detection rate, mAP, increased by about 10% to 22.2mAP, and the number of parameters decreased by about 70% to 2,147,589.

Developed for specific missions, CNNs have gradually improved the performance of object classification networks by using various architectures. The weight of the convolutional layer is a crucial factor in feature extraction. However, as the number of layers increases, performance degradation can occur due to problems such as the vanishing gradient. To overcome this problem, networks have evolved to continuously incorporate information from previous feature maps using various attention mechanisms. In this study, a Channel-wise Similarity Attention (CSA) method is proposed to measure the similarity of feature maps between channels and enhance positive information by highlighting it. Additionally, a deformable convolutional kernel is embedded to apply a flexible receptive field around the object area in the image, replacing the fixed receptive field of the conventional CNN layer. The network is trained end-to-end to classify the condition of vehicles on the road using collected drone flight images. The proposed model achieves an accuracy of 86.13% and 302 frames per second with a number of parameters of 1,273,504.

Recently advanced vehicle re-identification frameworks are mainly based on convolutional neural networks (CNN) and labeled information. Previous frameworks face two difficulties. First CNN includes complicated architectures, which require expensive GPU devices to perform computation. The second difficulty is that annotating vehicle identities for every frame is expensive and time-consuming. To tackle these two difficulties, this study proposes a simple but effective method to perform re-ID without CNN and labeled identities. The proposed method has two streams of vehicle re-identification. The object detector takes charge of detecting vehicles on the road. With the position of vehicles in the image, the condition module extracts the vehicle movement information and sets the condition to match the same vehicle between current and subsequent frames. To train the object detector and test the proposed algorithm, a set of drone flight images collect and annotate for studying the traffic road. It contains 9,776 train images and 2,200 test images for object detection. In the experiments, three different traffic video clips were applied for testing the proposed method.