Blockage of pedestrians will cause inaccurate people counting, and people’s heads are easily blocked by each other in crowded occasions. To reduce missed detections as much as possible and improve the capability of the detection model, this paper proposes a new people counting method, named Soft-YoloV4, by attenuating the score of adjacent detection frames to prevent the occurrence of missed detection. The proposed Soft-YoloV4 improves the accuracy of people counting and reduces the incorrect elimination of the detection frames when heads are blocked by each other. Compared with the state-of-the-art YoloV4, the AP value of the proposed head detection method is increased from 88.52 to 90.54%. The Soft-YoloV4 model has much higher robustness and a lower missed detection rate for head detection, and therefore it dramatically improves the accuracy of people counting.

Urban waterlogging is mainly caused by garbage clogging the sewer manhole covers. If the amount of garbage at a sewer manhole cover can be detected, together with an early warning signal when the amount is large enough, it will be of great significance in preventing urban waterlogging from occurring. Based on the YOLOX algorithm, this paper accomplishes identifying manhole covers and garbage and building a flood control system that can automatically recognize and monitor the accumulation of garbage. This system can also display the statistical results and send early warning information. During garbage identification, it can lead to inaccurate counting and a missed detection if the garbage is occluded. To reduce the occurrence of missed detections as much as possible and improve the performance of detection models, Soft-YOLOX, a method using a new detection model for counting, was used as it can prevent the occurrence of missed detections by reducing the scores of adjacent detection frames reasonably. The Soft-YOLOX improves the accuracy of garbage counting. Compared with the traditional YOLOX, the mAP value of Soft-YOLOX for garbage identification increased from 89.72% to 91.89%.

In view of the known problems of parameter sensitivity, local optimum, and slow convergence in the ant colony optimization (ACO), we aim to improve the performance of the ACO. To solve the traveling salesman problem (TSP) quickly with accurate results, we propose a fully parallel ACO (FP‐ACO). Based on the max–min ant system (MMAS), we initiate a compensation mechanism for pheromone to constrain its value, guarantee the correctness of results and avoid a local optimum, and further enhance the convergence ability of ACO. Moreover, based on the compute unified device architecture (CUDA), the ACO is implemented as a kernel function on a graphics processing unit (GPU), which shortens the running time of massive iterations. Combined with the roulette wheel selection mechanism, FP‐ACO has powerful search capabilities and is committed to obtaining better solutions. The experimental results show that, compared with the effective strategies ACO (ESACO) that runs on CPU, the speed‐up ratio of the proposed algorithm reaches 35, and the running time is less than that of the max–min ant system‐roulette wheel method‐bitmask tabu (MMAS‐RWM‐BT) that runs on GPU. Furthermore, our algorithm outperforms the other two algorithms in the speed‐up ratio and less runtime, proving that the proposed FP‐ACO is more suitable for solving TSP.

The 3D reconstruction experiment can be performed accurately in most cases based on the structure from motion (SfM) algorithm with the combination of the multi-view stereo (MVS) framework through a video recorded around the object. However, we need to artificially hold the camera and stabilize the recording process as much as possible to obtain better accuracy. To eliminate the inaccurate recording caused by shaking during the recording process, we tried to fix the camera on a camera stand and placed the object on a motorized turntable to record. However, in this case, the background did not change when the camera position was kept still, and the large number of feature points from the background were not useful for 3D reconstruction, resulting in the failure of reconstructing the targeted object. To solve this problem, we performed video segmentation based on background matting to segment the object from the background, so that the original background would not affect the 3D reconstruction experiment. By intercepting the frames in the video, which eliminates the background as the input of the 3D reconstruction system, we could obtain an accurate 3D reconstruction result of an object that could not be reconstructed originally when the PSNR and SSIM increased to 11.51 and 0.26, respectively. It was proved that this algorithm can be applied to the display of online merchandise, providing an easy way for merchants to obtain an accurate model.