Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called a rotatable region-based residual network (R 3 -Net), to detect multioriented vehicles in aerial images and videos. More specially, R 3 -Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In R-DN, a novel rotatable positionsensitive pooling is designed to keep the position and orientation information simultaneously while downsampling the feature maps of R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image data sets, namely, DLR 3K Munich Data set and VEDAI Data set, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://youtu.be/xCYD-tYudN0.

The intake of edible oil containing trans-fatty acids has deleterious effects mainly on the cardiovascular system. Thermal processes such as refining and frying cause the formation of trans-fatty acids in edible oil. This study was conducted to investigate the possible formation of trans-fatty acids because of the heat treatment of soybean oil. The types of trans-fatty acids in heated soybean oil are determined by attenuated total reflectance Fourier transform infrared spectroscopy and gas chromatography-mass spectrometry methods. The effects of the heating temperature on the trans-fatty acids in soybean oil were evaluated using gas chromatography flame ionization detection analysis. Results show that heat treatment at 240 °C causes the formation of trans-fatty acids in soybean oil and the amount of trans-fatty acids increases with heating time. The only peak observed at 966 cm(-1) of the samples indicates the formation of nonconjugated trans isomers in the heated soybean oil. The major types of trans-fatty acids formed were trans-polyunsaturated fatty acids. Significant increases (P < 0.05) in the amounts of two trans-linoleic acids (C18:2-9c,12t and C18:2-9t,12c) and four trans-linolenic acids (C18:3-9c,12c,15t, C18:3-9t,12c,15c, and C18:3-9t,12t,15c/C18:3-9t,12c,15t) in soybean oil heated to temperatures exceeding 200 °C were compared with those of the control sample. The heating temperature and duration should be considered to reduce the formation of trans-fatty acids during thermal treatment.