esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
## 1 Introduction
Issues include detecting small distant vehicles in expressway surveillance images captured by wide-angle lenses as well as the problems of reduced accuracy and increased false negative rates in vehicle detection due to vehicle size discrepancies and occlusions during traffic congestion. Therefore, a method using an improved CrowdDet algorithm is proposed to better detect expressway traffic congestion. Firstly, to better detect congested vehicles in expressway surveillance, the improved algorithm is proposed by using CrowdDet [20] as the baseline detector, introducing the Involution operator [21] with long-distance interaction and the bi-directional feature pyramid network (BiFPN) module [22] for multi-scale vehicle detection, and the improved algorithm is named IBCDet. This can provide an important basis for subsequent traffic congestion detection. Secondly, in order to quantify the degree of traffic congestion, the IBCDet algorithm adds tracking technology to measure the speed of vehicles, and it combines the Chinese expressway LoS criteria to divide the speed of vehicles and then analyze and determine the degree of congestion on the road section. Finally, in order to verify the performance of the proposed method, this paper uses the Nanjing Raoyue expressway surveillance video dataset (referred to as NJRY) to train and test the model. Experimental results show that compared with commonly used object detection algorithms, this method can achieve the best performance in both vehicle detection accuracy and traffic congestion detection accuracy. In summary, the contributions of this paper are as follows:  
* The proposal of an improved version of the CrowdDet algorithm (IBCDet) that incorporates the Involution operator and BiFPN modules, leading to enhanced long-distance vehicles and occluded vehicles detection accuracy in expressway surveillance scenarios. * The development of a vehicle-tracking algorithm based on IBCDet that calculates the running speed of vehicles and utilizes the Chinese expressway LoS criteria for traffic congestion detection. * Extensive experiments conducted on the self-built NJRY dataset and the public UADETRAC dataset, which demonstrate the superior performance of the proposed IBCDet algorithm in both vehicle detection accuracy and traffic congestion detection accuracy compared to commonly used object detection algorithms.
## 2 Methods
### Signal Processing
Within a set of approximate normal probability distribution data, about 99.7% of the values were distributed within 3 times of the standard deviation range, about 95% of the values were within 2 times of the standard deviation range, and about 68% were within 1 time of the standard deviation range. Assuming that a set of stationary horizontal signals was 10 and the standard deviation was 2, the range within 3 times of the standard deviation was equal to 10 \(\pm\) 6 or 4-16, and the value of +16 was set to CT, as shown in Equation (6). \[\sigma=\sqrt{\frac{1}{m}\sum_{i=m}^{2m-1}\left(S_{i}^{\prime}-Q\right)^{2}} \tag{5}\]  
\[CT=Q+3\sigma \tag{6}\]  
This study used CT to capture the start and end positions of the threshold beyond and expressed it as a segment from a point to b point. The starting position higher than the threshold value was set to point a, and the ending position lower than the threshold value was set to point b. As shown in Equation (7), the average value calculated for this section was taken as the average of the amplitude and time. In the VL and VMO sections, the segment between points of a and b was the same, and the average intensity of the VL and VMO myoelectric signals was calculated in the same section. To evaluate the difference in the average intensity of the signals between the two, the composite signal was calculated for evaluation which could visually show each other's relevance. As shown in Equation (8), the results of the VMO VL Ratio (VVR) were calculated to compare the sum of intramuscular muscles signal strengths of the two groups, where the average number of electrical signals \(\overline{VMO}_{ab}\) of VMO was compared with that of \(\overline{VL}_{ab}\) of VL. When VVR is greater than 1, the average VMO EMG signal was higher than VL, which indicates the VMO of the rehabilitation training action that was higher than VL should be able to achieve the purpose of rehabilitation. Conversely, less than 1 meant that the VL EMG average was higher than VMO [28]. \[\overline{S}_{ab}=\frac{1}{b-a\ +1}\sum_{i=a}^{b}S_{i}^{\prime},S_{a}^{\prime},S_{ b}^{\prime}\geq CT \tag{7}\]  
\[VVR=\frac{\overline{VMO}_{ab}}{\overline{VL}_{ab}} \tag{8}\]
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 5 Efficient Depth-Based MAC Protocol  
In this section, we first brief the basic ideas of ED-MAC followed by describing each operational phase in detail. We next discuss how ED-MAC handles newcomer nodes, and finally analyse the offered traffic upper-bound and also the minimum number of slots required in our proposed protocol.
## 3 Result and Discussion
### Performance of Teaching Data with Only Biomechanics Materials
#### 3.1.1 Simple Data Set  
The first analysis method, the L-RFA dataset of artificial bones, was analyzed as teaching data without additional processing. The predicted results for cadaver bones are shown in Figure 3. Figure 3a shows the correlation between the measured and predicted values, with the solid line indicating the ideal result and the measured and predicted values match. Figure 3b is a Bland-Altman plot, which is a method used to evaluate the agreement between two measurement methods. In this study, the agreement between the measured and predicted values was evaluated. The horizontal axis represents the measured and predicted mean values for each data point, and the vertical axis represents the difference between them. The dotted line in the figure represents the limits of agreement, which is the statistical 95% confidence interval obtained by subtracting the standard deviation multiplied by 1.96 from the mean of the difference between the two datasets, which can be interpreted as equivalent if found within this dotted line. As shown in Figure 3a, the coefficient of determination \(R^{2}=-0.144\) indicates no relationship. The four explanatory variables used were peak frequency (1000-5000 Hz), peak frequency (30-20,000 Hz), variance (500-1000 Hz), and skewness (500-1000 Hz). The Bland-Altman plot in Figure 3b shows that there was a weak fixed error of up to 0.4 on the horizontal axis, followed by a proportional error. This result indicates that the influence of random errors is small and that systematic errors due to fixed and proportional errors lead to lower prediction accuracy. Therefore, by correcting for this uniform error in advance, it is possible to predict implant stability in cadaveric bone using L-RFA data of artificial bones obtained at the laboratory level. However, it is preferable to avoid adding further data processing to correct errors because of concerns regarding generalizability and prediction accuracy degradation. Therefore, the improvement in the prediction accuracy should be investigated by devising teaching data in advance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Analysis Scheme** & \begin{tabular}{c} **Number of** \\ **Explanatory Variables** \\ \end{tabular} & \
\begin{tabular}{c} **Determination** \\ **Coefficient** \\ \end{tabular} &
\begin{tabular}{c} **Mean Squared** \\ **Error (MSE)** \\ \end{tabular} \\ \hline Simple data set & 4 & \(-0.144\) & 0.0275 \\ Data augmentation & 2 & \(-0.230\) & 0.305 \\ by linear interpolation & 2 & \(-4.38\) & 1.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results obtained using the three analysis schemes with only the artificial bone dataset.
## 3 Push-Out Tests and Analysis of Results
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen (Eight Bolts)**} & \multirow{2}{*}{
\begin{tabular}{c} **Ultimate** \\ **Force,** \(F_{\mathit{nit}}\) **(kN)** \\ \end{tabular} } & \multicolumn{2}{c}{**Slip (mm)**} & \multicolumn{2}{c}{**Average Value**} & \multicolumn{2}{c}{**Diff. (S\#/S1)**} \\ \cline{3-10}  & & **Total,** & \(\delta_{\mathit{nit}}\) & **Initial,** & **Total Force** & **To Failure Slip,** & **Force** & **Slip** \\ \hline \multirow{3}{*}{S1} & \#1 & 1644.3 & 12.94 & & & & & \\ \cline{2-10}  & \#2 & 1644.2 & 11.40 & 0.80 & 1653.4 & 11.87 & 100 & 100 \\ \cline{2-10}  & \#3 & 1671.8 & 13.66 & & & & & \\ \hline \multirow{3}{*}{S2} & \#1 & 808.2 & 7.34 & & & & & \\ \cline{2-10}  & \#2 & 745.8 & 6.99 & 0.96 & 789.6 & 6.03 & 47.8 & 50.8 \\ \cline{2-10}  & \#3 & 814.9 & 6.65 & & & & & \\ \hline \multirow{3}{*}{S3-20} & \#1 & 932.3 & 5.34 & & & & & \\ \cline{2-10}  & \#2 & 882.9 & 4.64 & 0.73 & 904.0 & 4.27 & 54.7 & 36.0 \\ \cline{2-10}  & \#3 & 896.9 & 5.03 & & & & & \\ \hline \multirow{3}{*}{S3-45} & \#1 & 1365.5 & 9.39 & & & & & \\ \cline{2-10}  & \#2 & 1398.9 & 11.07 & 0.79 & 1418.0 & 9.53 & 85.8 & 80.3 \\ \cline{2-10}  & \#3 & 1489.6 & 10.50 & & & & & \\ \hline \multirow{3}{*}{S4-20} & \#1 & 1549.6 & 7.86 & & & & & \\ \cline{2-10}  & \#2 & 1491.1 & 6.95 & 1.01 & 1552.4 & 6.91 & 93.9 & 58.2 \\ \cline{2-10}  & \#3 & 1616.6 & 8.95 & & & & & \\ \hline \multirow{3}{*}{S4-45} & \#1 & 1,830.8 & 9.28 & & & & & \\ \cline{2-10}  & \#2 & 1779.3 & 8.98 & 1.00 & 1730.0 & 7.76 & 104.6 & 65.4 \\ \cline{1-1} \cline{2-10}  & \#3 & 1579.9 & 8.02 & & & & & \\ \hline \hline \end{tabular} The maximum slip displacement was calculated at 90% of the ultimate load after reaching the ultimate load.  
\end{table} 
Table 2: Test results. by exceeding 6 mm, which is the minimum amount of ductile deformation listed in the road bridge design standards of South Korea. However, it is necessary to induce brittle bolt failure via ductile fracturing in the embedded part. It appears that proposed S4 model can adequately replace the conventional welded stud method in terms of strength if the brittle fracturing of the fastening part due to bolt failure is not a good approximation of the brittle fracture. The reason is that the brittle fracture is not a good approximation of the brittle fracture. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Fracture Mode** & **Concrete Slab** & **Steel Girder** \\ \hline S1 & & Stud shank–welding bead interface (ductile fracture) & \\ S2  
tension is improved. It is thought that this model will be very useful for deconstructing and reconstructing worn bridge slabs because the slab concrete can be easily demounted. As shown in Figure 11, the slip displacement caused by the initial repeated load was set as the initial value (\(\delta_{init}\)), and the slip displacement (\(\delta_{u}\)) during maximum shear failure was defined as a displacement amount corresponding to 90% of the failure load \(F_{ult}\) (Equation (1)). \[\text{Total slip},\delta_{u,tot}=\delta_{init}+\delta_{u} \tag{1}\]
## 4 Experimental Research on the Electrochemical Machining of an Inner Fillet
### Processing Example  
For the experimental workpiece shown in Figure 5(a), the actual processing effect was verified according to the target fillet radius of 0.4 mm, 0.5 mm, 0.6 mm, 0.8 mm, and 1 mm. According to Formula (2), the processing time required for the corresponding fillet radius was calculated and rounded, as shown in Table 6.  
According to the processing time in Table 6, the experiment was repeated three times, and the experimental results and errors are shown in Table 7. The processing effect of the first experiment is shown in Figure 9. During actual machining, due to the different burr states of the intersecting hole and the influence of cathode positioning errors, there was a certain error between the actual machining radius and the target radius. The maximum value of the error was 4.2%, the minimum value was 2.1%, and the average value was 2.9%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} **Intersecting Hole Filled** \\ **Radius \(R\) (mm)** \\ \end{tabular} & \multicolumn{2}{c}{**Processing Time \(t\) (s)**} \\ \cline{2-3} \cline{5-4}  & **Calculated** & **Calculated** & **Test Value** \\ \hline
0.4 & 98.22 & 98 \\
0.5 & 127.99 & 128 \\
0.6 & 160.53 & 161 \\
0.8 & 238.53 & 239 \\
1.0 & 359.96 & 360 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental condition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **df** & **SS** & **MS** & **F** & **Significance F** \\ \hline Regression analysis & 2 & 1.4675 & 0.7338 & 1017.89 & \(5.2\times 10^{-15}\) \\ Residuals & 13 & 0.0094 & 0.0007 & & \\ Total & 15 & 1.4769 & & & \\ \hline \hline  & **Coefficients** & **Standard Error** & \(t\) **Stat** & \(p\)**-Value** & **Lower 95.0\%** & **Upper 95.0\%** \\ \hline Intercept & 0.0122 & 0.0229 & \(-0.5327\) & 0.6032 & \(-0.037\) & 0.062 \\ \(t^{2}\) & \(-4.6\times 10^{-6}\) & 3.9 \(\times 10^{-7}\) & \(-11.567\) & \(3.2\times 10^{-8}\) & \(-5.4\times 10^{-6}\) & \(-3.7\times 10^{-6}\) \\ \(t\) & 0.0044 & \(2.1\times 10^{-4}\) & 21.467 & \(1.6\times 10^{-11}\) & 0.004 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Regression analysis of the fillet radius and processing time with a 10 V voltage.
## 2 Antenna Design Procedure and Methods
### Design of the Waffle Grid and Waveguide Transitions  
Owing to the large distance (L = 13 mm, about 1.2\(\lambda_{0}\) at 28 GHz) between adjacent antenna units, grating lobes occur at Ka-band with high levels. To reduce the grating lobe levels, a waffle grid, whose structure is shown in Figure 9a, was designed to increase the effective aperture area of the array unit cell. More details about the mechanism of the waffle grid to mitigate grating lobes can be found in [19].  
To facilitate the connection of the proposed array with testing equipment, two waveguide stepped impedance transitions were designed to connect the respective inputs of the Ka- and K-band arrays to standard WR-28 and WR-42 waveguide ports. The Ka- and K-band transition structures are shown in Figure 9b,c, respectively. For each structure, the thickness of the transition wall is 0.5 mm.
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 2 Materials and Methods
### Methods
#### 2.2.3 DPC PWV Retrieval Strategy  
For any pixel to be retrieved, we can simulate 81 water vapor absorption transmittances in the 910 nm band for different water vapor contents (PWV range of 0-8 cm, interval of 0.1 cm) according to the pre-constructed lookup table and the corresponding solar zenith angle and observation zenith angle of the pixel and then calculate the water vapor absorption transmittance in the DPC 910 nm band according to Formula (3) (for convenience of description, it is called \(T_{1}\)). Then, we can realize water vapor retrieval based on the simulated and actual WVAT in the 910 nm channel by means of linear interpolation. The specific process of retrieving water vapor by means of interpolation is as follows: two adjacent water vapor absorption transmittances are found from the 81 water vapor absorption transmittances simulated above, one of which is not less than \(T_{1}\) (called \(T_{2}\)) and the other is not greater than \(T_{1}\) (called \(T_{3}\)), after which we can perform water vapor inversion according to Equation (4) [37].  
\[V_{1}=V_{2}+\frac{(V_{3}-V_{2})\times(T_{1}-T_{2})}{(T_{3}-T_{2})} \tag{4}\]  
where \(V_{1}\) represents the PWV retrieval results, and \(V_{2}\) and \(V_{3}\) represent PWV corresponding to \(T_{2}\) and \(T_{3}\), respectively.
## 3 Method  
The flowchart illustrating the proposed method is presented in Figure 2. Initially, the multispectral image data utilized in this research comprises AHI data obtained from the Himawari-8 satellite, while the ground truth data for fire points is acquired from the meteorological satellite ground station located in Guangzhou, Guangdong, China. Initially, the collected data are preprocessed to obtain the six spectral bands necessary for creating the fire spot dataset. This dataset is subsequently split into the training and validation sets. The training data are utilized to train the proposed model as well as the comparison methods. Once the model is trained and saved, the validation set is employed to assess the performance of each model, leading to a comparative analysis using quantitative evaluation metrics.  
Figure 1: Research area.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 3 The Proposed Scheme: RSNA  
### Motivation  
In maritime opportunistic networks, vessels acting as networking nodes are randomly deployed over the sea. The communication links are significantly affected by sparse scattering, sea wave movement, and the ducting effect over the sea surface [20], due to the extreme complexity of the marine environment. Therefore, the impact of marine environmental factors on networking communication cannot be ignored. In this paper, we introduce the marine channel propagation model mentioned in our previous work [21] into opportunistic networks for data transmission.  
Routing and forwarding issues are the main focus for communication in OpSpNets; that is, it is necessary to find the desirable route to the destination. As shown in Figure 2, assuming that source node is S, the destination node is D. Nodes N1, N3 are the closet nodes of S toward the destination, they will relay the packets. After this hop, N1 and N3 get higher priority to be the relays. However, some seemingly qualified nodes of N1 and N5 of N3' next hop have no further available candidates to relay the packets, and only end up dropping the packets in a time period. The node N8 is similar to this. Actually, if node N2 gets the chance to deliver the packets, node D can eventually receive the packets through the route N2-N4-N7. The route requests of some seemingly qualified nodes may cause the packets to be delayed indefinitely and greatly degrade network performance. Therefore, solving the above problems is our target in this paper.
# _k_-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
## 2 Materials and Methods
### Evaluation Metrics  
Since the superclass classification problem of the PTB-XL ECG dataset is multi-label classification, we used the following five evaluation metrics to evaluate the model performance.  
* Accuracy: The percentage of correctly classified examples \[\text{Accuracy}=\frac{TP+TN}{FP+FN+TP+TN}\] (3) where \(TP\), \(TN\), \(FP\), \(FN\) represent the number of true positives, true negatives, false positives, and false negatives, respectively;
* Precision: The fraction of true positives among all predictions of a given class \[\text{Precision}=\frac{TP}{FP+TP}\] (4)
* Recall: the percentage of examples in a given class that the model correctly predicts as belonging to that class \[\text{Recall}=\frac{TP}{FN+TP}\] (5)
* The \(F_{1}\)-score: The harmonic mean of precision and recall is the \(F_{1}\)-score, which is an overall assessment of the quality of a classifier's predictions \[F_{1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\] (6)
* Exact match (subset accuracy): This is a metric of indicating the correct ratio for all labels. There is a disadvantage of ignoring partially correct predictions because they must all be matched. Additionally, because of this, the dependency on the label is taken into account. \[\text{Exact Match}=\frac{1}{N}\sum_{1}^{N}I(\hat{y}_{i}=y_{i})\] (7) where \(N\), \(\hat{y}_{i}\), \(y_{i}\) represent the total number of samples in test dataset, classifier's prediction, and true label, respectively.
# Integrated Adaptive Steering Stability Control for Ground Vehicle with Actuator Saturations
## 3 Integrated Controller Design
Following a similar procedure as in Step 2, we can choose \(V_{4}=\frac{1}{2}e_{3}^{2}+\frac{1}{2}e_{4}^{2}+\frac{1}{2}\bar{\theta}_{I}^ {2}\) as a Lyapunov function and take  
\[\dot{x}_{3} = x_{4},\quad\dot{x}_{4}=\frac{1}{I_{x}}(-d(F_{sdl}+F_{ssl})+d(F_{ sdr}+F_{ssr})+F_{y}h_{g}+u_{\theta})\]4 \[\dot{x}_{3} = x_{4},\quad\dot{x}_{4}=\frac{1}{I_{x}}(-d(F_{sdl}+F_{ssl})+d(F_{ sdr}+F_{ssr})+F_{y}h_{g}+u_{\theta})\]5  
with the projection-type adaptation law [29, 30, 31]:  
\[\dot{x}_{3} = x_{4},\quad\dot{x}_{4}=\frac{1}{I_{x}}(-d(F_{sdl}+F_{ssl})+d(F_{ sdr}+F_{ssr})+F_{y}h_{g}+u_{\theta})\]6 \[= \left\{ \begin{array}{ll}0&if\ \hat{\theta}_{I}=\theta_{I\max} \ and\ r_{2}e_{4}(f_{\theta}(x_{\theta},\delta_{f})+u_{\theta})>0\\ 0&if\ \hat{\theta}_{I}=\theta_{I\min}\ and\ r_{2}e_{4}(f_{\theta}(x_{\theta}, \delta_{f})+u_{\theta})<0\\ r_{2}e_{4}(f_{\theta}(x_{\theta},\delta_{f})+u_{\theta})&\ otherwise\end{array} \right.\]  
where \(k_{3}>0\), \(k_{4}>0\) and \(r_{2}>0\) are design parameters, and \(\theta_{I\max}\) and \(\theta_{I\min}\) are the known maximum and minimum values of \(\theta_{I}\); then, we have  
\[\dot{V}_{4}=-k_{3}e_{3}^{2}-k_{4}e_{4}^{2}\leq 0.\]  
Thus, \(e_{3}\), \(e_{4}\), and \(\dot{\theta}_{I}\) are bounded; taking the derivative of \(\dot{V}_{4}\) further yields \(\bar{V}_{4}=-2k_{3}e_{3}(e_{4}+x_{4r})-2k_{4}e_{4}(\theta_{I}(f_{\theta}(x_{ \theta},\delta_{f})+u_{\theta})-\dot{x}_{4r})\), which is bounded on the condition that \(\dot{x}_{3r}\) is bounded. Hence, \(\dot{V}_{4}\) is uniformly continuous, and consequently \(e_{3}\), \(e_{4}\to 0\), as \(t\rightarrow\infty\) with Lyapunov-like lemma. After \(u_{z}\) and \(u_{\theta}\) are determined, the active suspension control input can be obtained as follows:  
\[u_{I}=\frac{du_{z}+u_{\theta}}{2d},\quad u_{r}=\frac{du_{z}-u_{\theta}}{2d}. \tag{13}\]  
Step 3: for simplicity, let \(f_{\gamma}(x_{\gamma},\delta_{f})=2(b_{c}-ac_{f})x_{9}-\frac{2(a^{2}c_{f}+b^ {2}c_{c})}{v}x_{10}+2 ac_{f}\delta_{f}\), where \(x_{\gamma}=(x_{9},x_{10})^{T}\); then, Equation (7) can be simplified as  
\[\dot{x}_{10}=\frac{1}{I_{z}}(f_{\gamma}(x_{\gamma},\delta_{f})+M)\]  
where the saturated control input can be presented as  
\[M=sat(u_{y})=\text{sgn}(u_{y})\cdot\min\{|u_{y}|,u_{\max}\}, \tag{14}\]  
where \(u_{y}\) is the actuator demand input, \(sat(u_{y})\) is the actuator actual output, and \(u_{max}>0\) is the positive upper bound of the actuator. We now take the state tracing error of \(x_{10}\) as \(e_{10}=x_{10}-x_{10r}\), where \(x_{10r}\) is the reference value of \(x_{10}\). Let \(M_{\Delta}=M-u_{y}\), and pass it through a filter \(\dot{\zeta}=-k_{\zeta}\zeta+M_{\Delta}\), where \(k_{\zeta}\) is the tuning parameter. The filter acts as an anti-windup compensator, thus, the actuator saturation \(M_{\Delta}\) can be canceled out by feeding \(\zeta\) back into the nominal control input, \(u_{y}\)[26].
## 2 Materials and Methods
### Canny Edge Detection  
Edge detection is widely acknowledged as a valuable technique for extracting the boundaries of a certain image [57; 58].  
The Canny technique utilizes three factors to optimize edge detection, according to [59]:  
* Finding edges with minimal error: This standard requires the detector to measure the signal-to-noise ratio and captures the true edge precisely.
* Precise localization: This standard dictates that the operator must be as precise as feasible concerning the edge's center.
* A single response for a single edge: This criteria necessitates marking each edge point just once.  
The following phases were part of the conventional Canny edge detection process [60]:  
1. Applying a Gaussian filter to smooth the image: The image's noise is removed.
2. Calculating the gradient's intensity involves determining the gradient's direction.
3. Non-maximum suppression: The goal of this stage is to remove erroneous edge detection reactions.
4. To identify possible edges, use double thresholds: This phase involves two threshold levels. Th > Tl for both the low threshold level, Tl, and the high threshold level, Th. Ifa point's gradient value is greater than Th, it is regarded as an edge point. A point is not regarded as an edge point if the gradient value of that point is smaller than TL. The region surrounding the edge points will be considered when determining the edge points for points bigger than TI but smaller than Th.