YOLOFM: an improved fire and smoke object detection algorithm based on YOLOv5n.

To address the current difficulties in fire detection algorithms, including inadequate feature extraction, excessive computational complexity, limited deployment on devices with limited resources, missed detections, inaccurate detections, and low accuracy, we developed a highly accurate algorithm named YOLOFM. We utilized LabelImg software to manually label a dataset containing 18644 images, named FM-VOC Dataset18644. In addition, we constructed a FocalNext network, which utilized the FocalNextBlock module from the CFnet network. This improves the integration of multi-scale information and reduces model parameters. We also proposed QAHARep-FPN, an FPN network that integrates the structure of quantization awareness and hardware awareness. This design effectively reduces redundant calculations of the model. A brand-new compression decoupled head, named NADH, was also created to enhance the correlation between the decoupling head structure and the calculation logic of the loss function. Instead of using the CIoU loss for bounding box regression, we proposed a Focal-SIoU loss. This promotes the swift convergence of the network and enhances the precision of the regression. The experimental results showed that YOLOFM improved the baseline network's accuracy, recall, F1, mAP50, and mAP50-95 by 3.1%, 3.9%, 3.0%, 2.2%, and 7.9%, respectively. It achieves an equilibrium that combines performance and speed, resulting in a more dependable and accurate solution for detection jobs.

Research on 3D Reconstruction of Binocular Vision Based on Thermal Infrared.

Thermal infrared imaging is less affected by lighting conditions and smoke compared to visible light imaging. However, thermal infrared images often have lower resolution and lack rich texture details, making them unsuitable for stereo matching and 3D reconstruction. To enhance the quality of infrared stereo imaging, we propose an advanced stereo matching algorithm. Firstly, the images undergo preprocessing using a non-local mean noise reduction algorithm to remove thermal noise and achieve a smoother result. Subsequently, we perform camera calibration using a custom-made chessboard calibration board and Zhang's camera calibration method to obtain accurate camera parameters. Finally, the disparity map is generated using the SGBM (semi-global block matching) algorithm based on the weighted least squares method, enabling the 3D point cloud reconstruction of the object. The experimental results demonstrate that the proposed algorithm performs well in objects with sufficient thermal contrast and relatively simple scenes. The proposed algorithm reduces the average error value by 10.9 mm and the absolute value of the average error by 1.07% when compared with the traditional SGBM algorithm, resulting in improved stereo matching accuracy for thermal infrared imaging. While ensuring accuracy, our proposed algorithm achieves the stereo reconstruction of the object with a good visual effect, thereby holding high practical value.

More randomized and resilient in the topological properties of functional brain networks in patients with major depressive disorder.

Previous studies have reported the enhanced randomization of functional brain networks in patients with major depressive disorder (MDD). However, little is known about the changes of key nodal attributes for randomization, the resilience of network, and the clinical significance of the alterations. In this study, we collected the resting-state functional MRI data from 19 MDD patients and 19 healthy control (HC) individuals. Graph theory analysis showed that decreases were found in the small-worldness, clustering coefficient, local efficiency, and characteristic path length (i.e., increase of global efficiency) in the network of MDD group compared with HC group, which was consistent with previous findings and suggested the development toward randomization in the brain network in MDD. In addition, the greater resilience under the targeted attacks was also found in the network of patients with MDD. Furthermore, the abnormal nodal properties were found, including clustering coefficients and nodal efficiencies in the left orbital superior frontal gyrus, bilateral insula, left amygdala, right supramarginal gyrus, left putamen, left posterior cingulate cortex, left angular gyrus. Meanwhile, the correlation analysis showed that most of these abnormal areas were associated with the clinical status. The observed increased randomization and resilience in MDD might be related to the abnormal hub nodes in the brain networks, which were attacked by the disease pathology. Our findings provide new evidence to indicate that the weakening of specialized regions and the enhancement of whole brain integrity could be the potential endophenotype of the depressive pathology.

Task and Resting-State fMRI Reveal Altered Salience Responses to Positive Stimuli in Patients with Major Depressive Disorder.

Altered brain function in patients with major depressive disorder (MDD) has been repeatedly demonstrated by task-based and resting-state studies, respectively. However, less is known concerning whether overlapped abnormalities in functional activities across modalities exist in MDD patients. To find out the answer, we implemented an fMRI experiment and collected both task and resting-state data from 19 MDD patients and 19 matched, healthy, controls. A distraction paradigm involving emotionally valenced pictures was applied to induce affective responses in subjects. As a result, concurrent deficits were found in arousing activation during a positive task in both the reward circuit and salience network (SN) that is composed of the dorsal part of anterior cingulate cortex (dACC) and bilateral anterior insulae (AI) in only the MDD group. Subsequent amplitude of low frequency fluctuations (ALFF) and functional connectivity analyses based on resting-state data exhibited consistent alterations in the bilateral AI of MDD patients, and indicated patients' difficulties in regulating the balance between central executive network (CEN) and default mode network (DMN) due to altered connectivity among the CEN, DMN, and SN. Our findings provide new evidence demonstrating impaired salience processing and resulting alterations in responses to positive stimuli in MDD patients. Furthermore, brain abnormalities synchronized across functional states in MDD patients can be evidenced by a combination of task and resting-state fMRI analyses.