Deep neural networks (DNNs) have shown prominent performance in the field of object detection. However, DNNs usually run on powerful devices with high computational ability and sufficient memory, which have greatly limited their deployment for constrained environments such as embedded devices. YOLO is one of the state-of-the-art DNN-based object detection approaches with good performance both on speed and accuracy and Tiny-YOLO-V3 is its latest variant with a small model that can run on embedded devices. In this paper, Tinier-YOLO, which is originated from Tiny-YOLO-V3, is proposed to further shrink the model size while achieving improved detection accuracy and real-time performance. In Tinier-YOLO, the fire module in SqueezeNet is appointed by investigating the number of fire modules as well as their positions in the model in order to reduce the number of model parameters and then reduce the model size. For further improving the proposed Tinier-YOLO in terms of detection accuracy and real-time performance, the connectivity style between fire modules in Tinier-YOLO differs from SqueezeNet in that dense connection is introduced and fine designed to strengthen the feature propagation and ensure the maximum information flow in the network. The object detection performance is enhanced in Tinier-YOLO by using the passthrough layer that merges feature maps from the front layers to get fine-grained features, which can counter the negative effect of reducing the model size. The resulting Tinier-YOLO yields a model size of 8.9MB (almost 4× smaller than Tiny-YOLO-V3) while achieving 25 FPS real-time performance on Jetson TX1 and an mAP of 65.7% on PASCAL VOC and 34.0% on COCO. Tinier-YOLO alse posses comparable results in mAP and faster runtime speed with smaller model size and BFLOP/s value compared with other lightweight models like SqueezeNet SSD and MobileNet SSD.

In order to effectively identify and classify weld defects of thin-walled metal canisters, a weld defect detection and classification algorithm based on machine vision is proposed in this paper. With the weld defects categorized, a modified background subtraction method based on Gaussian mixture models, is proposed to extract the feature areas of the weld defects. Then, we design an algorithm for weld detection and classification according to the extracted features. Next, by using the weld images sampled by the constructed weld defect detection system on a real-world production line, the parameters of the weld defect classifiers are determined empirically. Experimental results show that the proposed methods can identify and classify the weld defects with more than 95% accuracy rate. Moreover, the weld detection results obtained in the actual production line show that the detection and classification accuracy can reach more than 99%, which means that the system enhanced with our proposed method can meet the requirements for the best real-time and continuous weld defect detection systems available nowadays.

Supply chain management (SCM) is a significant and complex system in a smart city that requires advanced artificial intelligence (AI) and optimization techniques. The multiobjective supply chain configuration (MOSCC) in SCM is to set the optimal configurations for supply chain members to minimize both the cost of goods sold ( $CoGS$ ) and the lead time ( $LT$ ). Although some algorithms have been proposed for the MOSCC, they do not make the best use of the problem-related knowledge and cannot perform well on the large-scale instances with many members and configuration options. Therefore, this article proposes a multipopulation ant colony system with knowledge-based local searches (MPACS-KLSs). First, the multiobjective algorithm is based on the multiple populations for multiple objectives framework. Two ant colonies are used to separately minimize $CoGS$ and $LT$ , which helps to search in the biobjective space sufficiently. Second, with the considerations of the problem-related knowledge, a priority-based solution construction method, a rank-based heuristic strategy, and an objective-oriented global pheromone updating strategy are proposed. Third, to speed up the convergence, especially for large-scale MOSCC instances, two knowledge-based local searches are designed to minimize $CoGS$ and $LT$ of solutions, respectively. Exhaustive experiments are conducted on both the instances from the real life and the randomly generated instances with different problem scales. The results show that MPACS-KLS is superior to the contestant algorithms, especially on the large-scale MOSCC instances, which significantly extends the AI and optimization techniques in practical applications of the smart city.

The random drift particle swarm optimization (RDPSO) algorithm is a PSO variant inspired by the free electron model in metal conductors placed in an external electric field. Based on the preliminary work on the RDPSO algorithm, this paper makes systematical analyses and empirical studies of the algorithm. Firstly, the motivation of the RDPSO algorithm is presented and the design of the particle’s velocity equation is described in detail. Secondly, a comprehensive analysis of the algorithm is made in order to gain a deep insight into how the RDPSO algorithm works. It involves a theoretical analysis and the simulation of the stochastic dynamical behavior of a single particle in the RDPSO algorithm. The search behavior of the algorithm itself is also investigated in detail, by analyzing the interaction among the particles. Then, some variants of the RDPSO algorithm are presented by incorporating different random velocity components with different neighborhood topologies. Finally, empirical studies of the RDPSO algorithm are performed by using a set of benchmark functions from the CEC2005 benchmark suite. Based on the theoretical analysis of the particle’s behavior, two methods of controlling the algorithmic parameters are employed, followed by an experimental analysis on how to select the parameter values, in order to obtain a satisfactory overall performance of the RDPSO algorithm and its variants in real-world applications. A further performance comparison between the RDPSO algorithm and other variants of PSO is made to prove the effectiveness of the RDPSO.