## 6 Conclusions, Limitations, and Future Work  
In this research, we developed a camera-based vehicle blind spot detection system through the FCN. The established research framework was composed of five stages: data preprocessing, feature extraction, FCN model learning, vehicle blind spot setting, and false positive reduction. Overall, 99.45% training accuracy and 98.99% testing accuracy of the FCN model were achieved, respectively. After deploying the software on the embedded board for actual testing on a real road, we confirmed 93.75% average blind spot detection accuracy with three false positives. The main expected effect is commercialization, which may replace the existing radar sensor-based blind spot detection system  
Figure 13: Experimental result of the second testing scenario. so that cost saving can be expected from the view point of the automobile manufacturer. In addition, this developed system can be applied to future autonomous vehicles. However, we encountered a set of limitations through conducting this research. The first research limitation refers to image datasets. Not enough images were extracted and preprocessed, and our own image datasets could not be secured due to time constraints. Testing with live video images was the second research limitation. Testing accuracy with live video images should have been evaluated mechanically similar to training and testing accuracy on the cloud platform, and we are still finding a way for this. In addition, testing was not conducted on a variety of weather conditions. The third research limitation was performance of the system. Although the system showed more satisfactory performance than our expectations, we are eager to show 100% guaranteed system performance without any false positives. The last and biggest research limitation was that we did not apply representation/feature learning and adopt any other deep learning methodologies such as CNN, Recurrent Neural Network (RNN), etc. for comparison analysis. All these research limitations would be future research work as described below. 1. More than ten thousand images including vehicles, motorcycles, and any other non-vehicle objects will be secured for both training and testing. By doing so, training and testing accuracy of a deep learning model will be improved. In addition, the system will be optimized for no false positives. 2. One of the issues during system testing was that camera angles continuously changed due to a strong wind while the vehicle was driving at high speed. Furthermore, image quality from the camera was not good enough. Hence, a camera which has better hardware specifications will be mounted with fixed angles underneath the side mirror, and further experiments under various conditions will be conducted. In addition, two cameras on both sides will be adopted for testing. 3. Target vehicles were detected by a rectangle box, but this would be replaced with a blind spot icon. Specifically, this icon will be displayed on the screen when target vehicles are detected in the blind spot. Moreover, the system will give both visual and audible alarms. 4. The reason for employing the FCN model in this research was that the adopted off-the-shelf embedded board had the power limit, thus this resulted in fewer cores and less parallelization. Due to this constraint, we decided to experiment the simplest one among a variety of deep learning methodologies. Moreover, the total amount of the images to be processed for classification was reduced by the sliding window technique. Since our goal was to identify the features of a vehicle in a particular area within a given image, this remarkably reduced the need of having complex networks with enhanced classification capabilities. However, it is highly meaningful to compare and analyze differences from the system developed on FCN basis by adopting different deep learning methodologies, so a new vehicle blind spot detection system will be developed by adopting various deep learning methodologies. 5.