Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism

Abstract

Petrochemical equipment detection technology plays important role in petrochemical industry security monitoring systems, equipment working status analysis systems, and other applications. In complex scenes, the accuracy and speed of petrochemical equipment detection would be limited because of the missing and false detection of equipment with extreme sizes, due to image quality, equipment scale, light, and other factors. In this paper, a one-stage attention mechanism-enhanced Yolov5 network is proposed to detect typical types of petrochemical equipment in industry scene images. The model considers the advantages of the channel and spatial attention mechanism and incorporates it into the three mainframes. Furthermore, the multiscale deep feature is fused with a bottom-up feature pyramid structure to learn the features of equipment with extreme sizes. Moreover, an adaptive anchor generation algorithm is proposed to handle objects with extreme sizes in a complex background. In addition, the data augmentation strategy is also introduced to handle the relatively small and extremely large sample and to enhance the robustness of the fused model. The proposed model was validated on the self-built petrochemical equipment image data set, and the experimental results show that it achieves a competitive performance in comparison with the related state-of-the-art detectors.

Petrochemical Equipment Tracking by Improved Yolov7 Network and Hybrid Matching in Moving Scenes

“…The parameters and experimental setting followed the original model and the pretrained model on COCO data set [41] were also employed. We follow the method [38] for details of some improved versions of these algorithms.…”

“…In this way, it can save parameters and computing power, and ensure that it can be integrated into the existing network architecture as a plug-and-play module. Previous Yolov5 was motivated by the attention mechanism in this way [38]; in practice, the mechanisms can be combined to be added to the existing network in a parallel or serial order.…”

Steel Strip Quality Assurance With YOLOV7-CSF: A Coordinate Attention and SIoU Fusion Approach

“…The rapid development of deep neural networks has greatly improved the effectiveness of object recognition technology. State-ofthe-art object detection techniques based on deep learning can be classified into two main types, namely, two-stage and one-stage methods, depending on how candidate regions are generated [7]. R-CNN [8] and its derivatives, like Faster R-CNN and Mask R-CNN [9], [10], enhance object detection accuracy by combining manual feature extraction with CNN-based learning in a two-stage process.…”

Visual Servo Kinematic Control of Delta Robot using YOLOv5 Algorithm

“…Based on Convolutional Neural Network (CNN) architecture, the YOLO can predict objects' class with associated probability across many categories and parallelly detect their positions within bounding boxes. Due to its fast and accurate detection capability, the YOLO algorithm has been used in various applications, for instance, precision livestock monitoring [48]- [49], face-expression identification [50] face detection in complex scene [51], realtime driver assistance system [47], [52]- [53], real-time multiple-object surveillance system of traffic scene [54] and tracking/detection of petrochemical equipment for static/moving scenes [55]- [56]. Even though, several visionbased systems using the YOLO-v4 and YOLO-v5 algorithms for object detection and recognition, incorporated with a 5-DOF serial-link arm [57], a cobot arm [58] or a YuMi dualarm collaborative robot [59], can detect and classify multiple objects with a small inference time as well as the robot gripper was able to reach the target or catch-point position of the specified object, but they did not impose any constraint on pick-and-place operational time or accuracy of graspingposition of the robot gripper.…”