Copyright © 2018 Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. International Journal of Engineering & Technology, 7 (4.44) (2018) 177-180 International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET Research paper Rear-Approaching Vehicle Detection using Frame Similarity base on Faster R-CNN Yeunghak Lee 1 *, Israfil Ansari 2 , Jaechang Shim 3 1 Andong National University 2 Andong National University 3 Andong National University *Corresponding author E-mail: yhsjh.yi@gmail.com Abstract In this paper, we propose a new algorithm to detect rear-approaching vehicle using frame structure similarity based on deep learning algorithm for use in agricultural machinery systems. The commonly used deep learning models well detect various types of vehicles and detect the shapes of vehicles from various camera angles. However, since the vehicle detection system for agricultural machinery needs to detect only a vehicle approaching from the rear, when a general deep learning model is used, a false positive is generated by a vehicle running on the opposite side (passing vehicle). In this paper, first, we use Faster R-CNN model that shows excellent accuracy rate in deep learning for vehicle detection. Second, we proposed an algorithm that uses the structural similarity and the root mean square comparison method for the region of interest(vehicles area) which is detected by Faster R-CNN between the coming vehicle and the passing vehicle. Experimental results show that the proposed method has a detection rate of 98.2% and reduced the false positive values, which is superior to general deep learning method. Keywords: faster r-cnn; vehicle detection; structural similarity index; deep learning; agricultural machine. 1. Introduction Many agricultural machinery systems are improving in perfor- mance due to the development of the industry. And the dependence of agricultural machinery on cultivating crops is increasing. The characteristics of farm machinery currently in use are that they move slower than cars. Since the use of agricultural machinery is used not only in rural areas but also in urban suburbs, many crashes and serious injuries occur every year. This paper proposes a new rear approaching vehicle detection algorithm for agricultural ma- chine systems to prevent accidents in agricultural machinery. Vehicle detection is used in many places such as public safety, pub- lic security, surveillance, intelligent traffic volume control, and au- tonomous driving. There are two kinds of vehicle detection: front view and rear view. The front view is the detection of a vehicle run- ning in front of a traveling vehicle. The rear view is the detection of a vehicle approaching from behind (or approaching with a fixed camera). The rear approaching vehicle detection method is divided into vision based and audio (or sound) based. In an audio-based study, Chen [1] detected a rear-access car using the natural fre- quency analysis of the car. Ananthanarayanan [2] extracted various types of features using sound (conversation, music, wind, automo- bile, etc.) as input data and developed a rear approaching vehicle detection system by analyzing the characteristics. The advantage of these systems is that they are low cost to manufacture and have low computational complexity. However, due to the use of sound (fre- quency), areas with noises can be highly susceptible to noise, and if the intensity of the frequency is weak, it may be exposed to danger because the rear approaching vehicle id detected very closely. Vision based vehicle detection research has focused on front-run- ning vehicle detection for use in autonomous vehicles. There is not much research on vehicle detection (frontal) approaching a camera installed behind a vehicle. In general, vehicle detection algorithms to obtain object features has been used a scale invariant feature transform (SIFT), histogram of oriented gradients (HOG), speed up robust features (SURF), Haar cascade, and Bigausian edge detec- tion (BED). Adaboost, Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) has been used to classify vehicles and other objects. These features lack the generalization ability to detect different objects. In addition, feature extraction is influenced by complicated and various illumination changes, camera view angle, and image complexity, and so on. These bad feature extractions can reduce the object detection rate and be difficult to apply in real time because of system degradation. Vision based rear approaching ve- hicle detection research has been focused on vehicle detection in blind spots of running vehicles. Chen [3] has used the one - dimen- sional distance information and two intersection times to detect the blind area approaching vehicle. The paper [4] detected vehicles in the blind spot using cascade classification through Adaboost learn- ing based on the modified census transformation feature vectors. In the above studies, vehicles detected as traveling and approaching vehicles with similar speed and at a short distance, so it is somewhat distant from this research. Deep learning technology is becoming more and more popular in the field of artificial intelligence. Especially, CNN is widely used in various fields such as image recognition, speech recognition, pe- destrian detection, face recognition, etc. Unlike general feature ex- traction systems, CNN uses raw images as inputs and extracts fea- tures through a large amount of training with high flexibility and generalization capabilities. It shows considerably higher object classification accuracy rate than traditional feature extraction method. CNN can be applied to object detection using region based R-CNN [5] model, which was greatly improved object detection