An encoder‐decoder based CNN architecture using end to end dehaze and detection network for proper image visualization and detection

Abstract

Industrial sectors are reinventing in automation, stability, and robustness due to the rapid development of artificial intelligence technologies, resulting in significant increases in quality and production. Visual‐based sensor networks capture various views of the surrounding environment and are used to monitor industrial and transportation sectors. However, due to unclean suspended air particles that damage the whole monitoring and transportation systems, the visual quality of the images is degraded under adverse weather conditions. This research proposed a convolutional neural network‐based image dehazing and detection approach, called end to end dehaze and detection network (EDD‐N), for proper image visualization and detection. This network is trained on real‐time hazy images that are directly used to recover dehaze images without a transmission map. EDD‐N is robust, and accuracy is higher than any other proposed model. Finally, we conducted extensive experiments using real‐time foggy images. The quantitative and qualitative evaluations of the hazy dataset verify the proposed method's superiority over other dehazing methods. Moreover, the proposed method validated real‐time object detection tasks in adverse weather conditions and improved the intelligent transportation system.

A Method for Identifying Power Equipment in Foggy Substation Based on Improved YOLOv4

“…The Cycle Dehaze [7] image defogging algorithm is an enhanced version of the Cycle GAN [8] . The deep separable convolution in the MobileNetv3 network greatly reduces the parameter size of the backbone network, which is then used to improve the YOLOv4 backbone feature extraction network CSPLocknet53.…”

Multispectral Pedestrian Detection Based on Prior-Saliency Attention and Image Fusion

“…In these scenarios, high-precision and -reliability object detection methods are required [1]. During daylight hours, contemporary object detection algorithms such as Yolo [2] and the fast R-CNN series [3] demonstrate commendable performance when operating on data from visible cameras. However, as illuminance conditions deteriorate, the information gleaned from visible images weakens, often becoming indistinguishable amidst background noise.…”