Leveraging haze-aware features for improved image clarity and detection accuracy with an optimized DCNN-YOLOv8 network.

Low visibility, less contrast, and warped object boundaries make current detection algorithms far less effective, making it impossible to find objects in foggy environments. This work builds a novel integrated architecture that includes temporally optimized deep feature extraction, haze-aware image clarity, and an enhanced YOLOv8 detector specifically intended for foggy environments to address these limitations. Differential characters of images that are hazy are compared to other natural images which restrict generalization of present Image Quality Assessment (IQA) algorithms. Initial intention of this work is designing and developing techniques for IQA and object detection with hazy outdoor images. Here, hazy input image is first directed deal to propose Haze aware Structural Pixel Neighbor (HSPN) features, Color rendition, and Mean Subtracted Contrast Normalized (MSCN) coefficients. Also, assessing image quality is done using Deep Convolutional Neural Network (DCNN) that is trained by proposed optimization algorithm, namely Chronological Chimp optimization Algorithm (CChOA). Moreover, this developed CChOA is formed by integrating the Chronological concept with Chimp Optimization Algorithm (ChOA). Furthermore, assessed image quality is further tends to object identification using You Only Look Once stage-9 (YOLOv8), which is based on Neural Network (NN). Finally, object is identified and performance of this model is enhanced by evaluating with three performance metrics, such as Mean Seismic Data Structural Similarity (MSDSS), Signal-to-Noise Ratio (SNR), and Structural Similarity Index (SSIM) with values of 0.944, 50.769, and 0.925, respectively.