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Feature pyramid network (FPN) is a typical structure in object detection. It can improve the accuracy of detection results by fusing feature information at different resolutions and enhancing the expression ability of different levels of features. Among them, the mismatch between the resolution of feature information and the receptive field and the limited way of feature fusion hinder the full exchange of feature information. To solve the above problems, this paper designs a new structure called an object detection feature pyramid network based on context information and an efficient bidirectional fusion (CB-FPN): (1) Before feature fusion, this study designs a context enhancement module with cross stage partial network (CSPNet) module (CEM-CSP). By using carefully designed dilated convolutions on high-level features, rich context information and receptive fields are obtained to match appropriate feature information. (2) In feature fusion, this study designed a bidirectional efficient feature pyramid network (BE-FPN) module to fuse features efficiently. After adding these two modified architectures to Faster R-CNN with ResNet-50, the average precision (AP) improves from 37.5 to 39.2 on COCO val-2017 data set. In addition, extensive experiments show the effectiveness of our methods on one-stage, two-stage, and anchor-free models. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion. [ABSTRACT FROM AUTHOR]