ABSTRACT
The development of port automation requires sensors to detect container movement. Vision sensors have recently received considerable attention and are being developed as AI advances, leading to various container motion detection methods. Faster-RCNN is a detection method that performs better precision and recall than other methods. Nonetheless, the detectors are set using the Faster-RCNN default parameters. It is of interest to optimized its parameters for producing more accurate detectors for container detection tasks. Faster RCNN requires mixed integer optimization for its continuous and integer parameters. Efficient Modified Particle Swarm Optimization (EMPSO) offers a method to optimize integer parameter by evolutionary updating the space of each candidate solution but has high possibility stuck in the local minima due to rapid growth of Gbest and Pbest space. This paper proposes two modifications to improve EMPSO that could adapt to the current global solution. Firstly, the non-Gbest and Pbest total position spaces are made adaptive to changes according to the Gbest and Pbest position spaces. Second, a weighted multiobjective optimization for Faster-RCNN is proposed based on minimum loss, average loss, and gradient of loss to give priority scale. The integer EMPSO with adaptive changes to Gbest and Pbest position space is first tested on nine non-linear standard test functions to validate its performance, the results show performance improvement in finding global minimum compared to EMPSO. This tested algorithm is then applied to optimize Faster-RCNN with the weighted cost function, which uses 1300 container images to train the model and then tested on four videos of moving containers at seaports. The results produce better performances regarding the speed and achieving the optimal solution. This technique causes better minimum losses, average losses, intersection over union, confidence score, precision, and accuracy than the results of the default parameters.
ABSTRACT
This paper presents new control designs and implementations of truck-trailer path following in forward and backward motions. The path following controls are designed in two modes, which are the controls with reference on the head-truck (RH-control) and with reference on the trailer (RT-control). Both modes aim to converge the distance and orientation errors of the head-truck as well as the trailer with respect to the desired path to zero. Using the designed controls, the asymptotic stabilities of the equilibrium points (i.e., error points equal to zeros) are analyzed using the Lyapunov method. The performances of RH-and RT-controls in controlling the truck-trailer are compared for forward and backward motions. The simulation results show that the RT-controls perform better than the RH-controls and the RT-controls can be applied for a curve-path following in both forward and backward directions. The experimental results of a prototype truck-trailer show the effectiveness of the proposed controls.
