Peplography is a technology for removing scattering media such as fog and smoke. However, Peplography only removes scattering media, and decisions about the images are made by humans. Therefore, there are still many improvements to be made in terms of system automation. In this paper, we combine Peplography with You Only Look Once (YOLO) to attempt object detection under scattering medium conditions. In addition, images reconstructed by Peplography have different characteristics from normal images. Therefore, by applying Peplography to the training images, we attempt to learn the image characteristics of Peplography and improve the detection accuracy. Also, when considering autonomous driving in foggy conditions or rescue systems at the scene of a fire, three-dimensional (3D) information such as the distance to the vehicle in front and the person in need of rescue is also necessary. Furthermore, we apply a stereo camera to this algorithm to achieve 3D object position and distance detection under scattering media conditions. In addition, when estimating the scattering medium in Peplography, it is important to specify the processing area, otherwise the scattering medium will not be removed properly. Therefore, we construct a system that continuously improves processing by estimating the size of the object in object detection and successively changing the area range using the estimated value. As a result, the PSNR result by our proposed method is better than the PSNR by the conventional Peplography process. The distance estimation and the object detection are also verified to be accurate, recording values of 0.989 for precision and 0.573 for recall. When the proposed system is applied, it is expected to have a significant impact on the stability of autonomous driving technology and the safety of life rescue at fire scenes.

Recently, with the widespread adoption of quick response (QR) code payments, there have been incidents of unauthorized use of QR codes presented at the time of payment, due to theft or duplication. As a countermeasure, conventional QR code payment systems are designed to update the QR code periodically. However, there is a problem: it is possible to make a payment using an illegally obtained QR code until the update. Therefore, it is necessary to encrypt the QR code itself to prevent its duplication. The objective of this research is to prevent fraudulent use of QR payments by combining image encryption using random numbers and Rivest Cipher 4 (RC4). In this paper, we perform image encryption using random numbers generated from a uniform distribution for QR codes presented at the time of payment and encrypt the seed value, which is the decryption key, using RC4. As a result, the proposed encryption method prevents unauthorized use of the QR code used for payment by stealing the image, and the processing speed and encryption strength are sufficient. Histogram analysis, key sensitivity analysis, and correlation coefficients were used to measure encryption strength. Finally, the proposed method is expected to enable more secure use of QR payments compared to conventional systems.