Study On Vehicle Image Segmentation Based On Pulse Coupled Neural Networks

ABSTRACT:Vehicle image segmentation is the most fundamental and important step in a vehicle detection system. The quality of the vehicle image segmentation has a direct impact on the accuracy and efficiency of the subsequent image processing. However, it is always of great importance for intelligence transportation field to investigate an accurate and practical vehicle image segmentation model under the natural light environment. The segmentation model based on human vision is the research direction of intelligent transportation system for information processing in the future.Under this background, a vehicle segmentation method with visual neuron characteristic has been studied on the basis of Pulse Coupled Neural Networks (PCNN) model from both theory and application. The main research contents and innovation contributions of this thesis are summarized as follows:(1) The vehicle image segmentation using the traditional PCNN model usually suffers from troubles of over-segmentation and under-segmentation in the license plate area. Under this circumstance, a method for choosing optimal parameters in PCNN model is proposed. The initial threshold of PCNN segmentation model is set with Otsu algorithm, the connection coefficients are updated using the mean square difference of neurons within the local area and Hebb rule is used to calculate the connection coefficient matrix. The experimental results demonstrate that the optimized PCNN model can reduce the number of iterations during image segmentation and enhance self-adaptive segmentation effect of PCNN model.(2) A vehicle shadow elimination model is proposed to reduce the interference from the shadows in the license plate segmentation.The model combine the optimized PCNN with the shadow attributes. Meanwhile, there is no need to construct the background model and the shadow model. By performing the segmentation on both gray and hue component, vehicle and shadow are separated in each information channel. The image with the removed shadow is finally obtained by merging the segmentation results of the two information channel. The experimental results show that the model not only eliminates the vehicle shadow, but also keeps more details of the license plate and the car logo. Shadow elimination rate further validate the effectiveness of the algorithm for eliminating shadows.(3) To handle the sunlight and diffuse reflection of the vehicle body, a Receptive Field-Pulse Coupled Neural Networks (RF-PCNN) model is proposed, where the feedback domain linking matrix is determined by neurons receptive field model. This new RF-PCNN model has both directivits and scales.The function of visual cells to segment an image can be simulated more efficiently. The experimental results show that the RF-PCNN model improves the effect of license plate image segmentation under natural environment. A high boundary detection rate has been achieved for the characters and the over-segmentation and under-segmentation problems have been solved in the vehicle image segmentation with complex backgrounds.(4) The segementation effects of the license plate are influenced by many factors, such as small proportion, unfixed locations, variant sizes and variant illuminations. Aiming at solving the above problems, an image segmentation method based on the Visual Attention Mechanism Pulse Coupled Neural Networks (VAMPCNN) is proposed. This model realizes multi-scale space image segmentation on the basis of the RF-PCNN model, which achieves better segmentation effect for multi-scale targets and overcomes the influence of small proportion and variant sizes of license plate on image segmentation.The model combines data-driven mode with the task-driven mode in the visual attention mechanism, Through the determination of the target’s characteristic scale and the optimal scale, it can locate the interesting targets in the multi-scale space. The experimental results show that the model has the function to position segmentation multi-targets at optimal scale.