Research on dense object detection methods in congested environments of urban streets and roads based on DCYOLO.

The urban street is a congested environment that contains a large number of occluded and size-differentiated objects. Aiming at the problems of the loss of the target to be detected and low detection accuracy resulting from this situation, a newly improved algorithm, based on YOLOv4, DCYOLO is proposed. Firstly, a Difference sensitive network (DSN) is introduced to extract the edge features of objects from the original image. Then, assign the edge features back to increase the edge intensity of the object in the original image and ultimately improve the detection performance. Secondly, the feature fusion module (CFFB) based on context information is introduced to realize the cross-scale fusion of shallow fine-grained features and deep-level features, to strengthen the cross-scale semantic information fusion of feature maps and eventually improve the performance of object detection. At last, in the network prediction part, the SIOU loss function replaces the original CIOU loss function to improve the convergence speed and accuracy of object detection. The experiments based on MS COCO 2017 and self-made datasets show that, compared with the YOLOv4, the detection accuracy of DCYOLO models is greatly improved with an increase of 9.1 percentage points in AP and 10.4 percentage points in APs. Compared with YOLOv5x and Faster R-CNN, DCYOLO shows higher accuracy and better detection performance. The experiment result proves that the DCYOLO algorithm can adapt to the dense object detection requirements in the congested environment of urban streets.

Stability analysis of the food delivery robot with suspension damping structure.

Aiming at the shock absorption and stability under complex terrain conditions, a six-wheeled food delivery robot with a suspension damping structure was designed. The food delivery robot is mainly composed of a take-out box, a chassis mobile structure and a suspension damping structure. The dynamic analysis of the suspension damping structure of the robot is carried out through the Lagrangian equation, and the offset of the wheels in different driving environments is obtained. Then, the zero-moment point method is used to analyze the stability of the food delivery robot in two non-horizontal movement environments of the left and right wheels and the front and rear wheels. Based on this, taking speed bumps and ramp terrain with different slopes as examples, the stability of the food delivery robot is analyzed by ADAMS simulation. The simulation and experiment results verify the rationality of the structure design of the food delivery robot with a suspension damping structure and the correctness of the theoretical analysis.