A Vehicle Detection Method Based on Convolutional Neural Network Optimized by Genetic Algorithm - Sun, Qingqing (International Conference on Intelligent Control and Information Processing (Online), 2024)
This manuscript introduces an advanced vehicle target detection algorithm which leverages the integration of improved underside shadow characteristics with a Convolutional Neural Network (CNN) optimized by genetic algorithms. Our approach augments underside shadow delineation through difference enhancement techniques to amplify the grayscale contrast between shadows and the road surface. This, in turn, facilitates the precise extraction of road boundaries, streamlining the computational process when generating potential vehicle shadow candidates. By harnessing both the geometric and textural properties of the vehicle shadows, our algorithm eficiently sifts through and formulates viable vehicle presumptions. The innovative application of a genetic algorithm to fine-tune the CNN addresses the common pitfalls of local optima encountered with conventional CNN training methodologies, such as the gradient descent approach, which often initializes weights randomly. This optimization yields a substantial increment in the accuracy of vehicle detection. The algorithm's superior performance is further substantiated by comprehensive validation on a vast collection of real-world roadway images within MATLAB. This rigorous testing confirms the algorithm's adeptness in differentiating between genuine vehicle underside shadows and other interfering regions, consequently ensuring exceptionally reliable vehicle detection. The system shows remarkable adaptability and robustness across a variety of complex environments, characterized by fluctuating illumination, multiple tra fic lanes, and vehicles exhibiting diverse positions and orientations.