Night vehicle detection method based on improving Mask RCNN

The traditional nighttime vehicle detection method is generally based on the extraction and identification of headlights, which is prone to misjudgment, detection accuracy, and real-time lack of deficiency. To address the above issue, a night vehicle detection algorithm based on improved Mask RCNN night vehicle detection (Mask RCNN) is studied. The normal convolution in the residual network (ResNet) structure is modified to a grouped convolution of 16 groups, and channel number superposition is achieved by 16 groups of 1×1 convolution Network parameters are reduced to 1/16 of a normal convolution. The detection speed is improved and achieves the same effect as normal convolution. The channel attention mechanism module (squeeze-and-excitation, SE) is embedded in the ResNet structure, two fully connected layers are used to build the bottleneck structure, normalized weights are weighted to each channel feature, The representational power of the network is enhanced; Bottom-up structures are added behind feature pyramid networks (FPNs), the strong localization information of the underlying features is passed to the high-level semantic features; Adaptive pooling layer is added. The region proposal network (RPN) generates candidate regions, which are subsequently assigned to feature maps at different scales by region. The jump connection structure is added between the bottom feature and the top feature at each stage, The model parameters are reduced, while retaining the global representational power of the model. The open-source dataset Microsoft common objects in context (MS COCO), and Berkeley Deep Drive 100K (BDD100K) contain some nighttime driving images, these images are image enhanced. A test set is constructed to evaluate the performance of the detection, it contains 2 000 images. The Mask RCNN-NVD algorithm is tested on the test set, the mean Average Precision of Mask RCNN-NVD is 92.62, and the Frames Per Second (FPS) of Mask RCNN-NVD is 30 frames. Compared with the original Mask RCNN algorithm, the mapped value is improved by 1.68 and the FPS value is improved by 4 frames. The proposed method is validated, and nighttime vehicle detection is improved in both accuracy and real time.