On Real-Time Object Recognition by Single Image Dehazing Method Using Deep Learning Approach
Shruti Pathak, Amit Doegar · 2023
In the past decade, various haze removal techniques have been widely reported for object recognition. But hitherto little has been identified on the use of single image dehazing using transfer learning approach for object detection. Single image dehazing is an emerging computer vision technology which offers some of the extreme benefits over the existing techniques such as consumes less processing time, requires less space for real time dehaze purpose, etc. In this study, we combine both the object detection and image dehazing methods for real time applications such as-remote sensing, video surveillances, driverless automatic vehicles, etc. This paper presents an effective and efficient image dehazing method using transfer learning which helps to recognize objects in real time with more clarity and that can automatically detect objects with a high recognition rate and lesser probability of error. Our tests show that object detection becomes less accurate as the haze intensity increases; yet, under all haze circumstances (low, medium, or heavy), our jointly trained model AOD-net +YOLO v3 consistently outperforms non-joint and naïve YOLO v3 techniques.