# Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments
## 4 Future Research Directions and Conclusions  
In this paper, we have presented a conceptual sensor fusion framework to detect objects even in dense smoke environments using a thermal IR camera, ultrasonic sensors, and an FMCW radar based on the review of several research papers. The proposed sensor fusion can provide new methodologies such as (1) 3D global position estimation for multiple objects through two types of 2D information and one type of 3D information, (2) application of image transformation theory to general data information transformation, (3) 3D position estimation of an object using 1D ultrasonic sensor information, (4) extension of the directional angle and detection area of the ultrasonic sensor system, (5) rejection of measurement error in ultrasonic sensors caused by temperature change in dense smoke, (6) object detection and classification through image processing of a thermal IR camera. The proposed system using these methodologies is expected to be able to detect objects even in environments with less than 1 m visibility, such as in dense smoke environments. There are some limitations of the proposed methodologies as well. Because thermal IR cameras cannot provide detailed images of the environment like RGB cameras, the environmental information from the images can be ambiguous. Most of all, because the sensors will be normally used in very high-temperature environments of dense smoke, the performance of object detection can be affected by image saturation in thermal IR cameras or detection error propagation in ultrasonic sensors and FMCW radar. These unwanted phenomena are normally caused by extremely high temperatures. As a result, the proposed sensor fusion system remains most predictable when the temperature specification of used sensors is as high as possible, but this is impossible due to the implementation costs and technical limitations. Nevertheless, the proposed sensor fusion will be more useful than other studies because the selected sensors are the most appropriate for the dense smoke environment, and the proposed methodologies maximize the strengths of each sensor as well as compensate for its weaknesses. In future work, we will complete the hardware and software shown in Figures 9 and 10 and the experimental setup to validate the theoretical methodology proposed in this paper. Then, we will evaluate the performance based on some experimental conditions and find more limitations of the proposed system. The obtained results will also need to be compared  
Figure 10: Concept of hardware implementation. to various existing methods. In addition, the computational cost will be studied because it is very important to reduce computational costs while maintaining or increasing algorithm performance simultaneously. Theoretically, the detection error of an ultrasonic sensor and radar is none, according to the theoretical analysis in this paper. However, in reality, the detection error of an ultrasonic sensor and radar depends on the sensor's specifications that the user chooses for their applications. Therefore, we need to study how much detection error by ultrasonic and radar sensors is available after completing the hardware setup. Most of all, the operating temperature range of all electric devices, including the sensors used in this paper, is normally \(-40\) to \(100\) degrees Celsius, and the temperature of dense smoke is more than \(500\) degrees Celsius. Because of that, the sensors cannot work normally at relatively high temperatures. Therefore, all applications for fire disasters, such as firefighting robots and firefighter aids, need cooling systems and/or countermeasures of heat resistance/insulation. The cooling system and the heat resistance/insulation system are other research topics in the field of firefighting platforms. The scope of this paper is not a study in any detail of cooling systems or heat resistance/insulation systems but a study of how to deal with sensors for object detection in dense smoke environments.