diff --git a/AlgorithmModule/include/CameraCheckAnalysisy.hpp b/AlgorithmModule/include/CameraCheckAnalysisy.hpp
index f566b4a..b4b1b8b 100644
--- a/AlgorithmModule/include/CameraCheckAnalysisy.hpp
+++ b/AlgorithmModule/include/CameraCheckAnalysisy.hpp
@@ -191,6 +191,10 @@ private:
     // 找到产品(亮色区域)所在的第一行(顶部边缘), 用于左右图上下对齐
     int GetProductFirstRow(const cv::Mat &img, int &outRow);
 
+    // 拟合产品顶边直线段: y = outK * x + outB, outP1/outP2 为线段两端点(图像坐标系)
+    // 返回 0 成功; 非 0 失败(未找到顶边或有效点太少)
+    int FitProductTopLine(const cv::Mat &img, float &outK, float &outB, cv::Point2f &outP1, cv::Point2f &outP2);
+
 public:
     // 运行的基本参数
     RunInfoST m_RunConfig;
diff --git a/AlgorithmModule/src/CameraCheckAnalysisy.cpp b/AlgorithmModule/src/CameraCheckAnalysisy.cpp
index 5939a9a..a624889 100644
--- a/AlgorithmModule/src/CameraCheckAnalysisy.cpp
+++ b/AlgorithmModule/src/CameraCheckAnalysisy.cpp
@@ -10,6 +10,7 @@
 #include "CheckUtil.hpp"
 #include "Define.h"
 #include <algorithm>
+#include <cmath>
 
 CameraCheckAnalysisy::CameraCheckAnalysisy()
 {
@@ -168,7 +169,7 @@ int CameraCheckAnalysisy::Detect_Pre()
 	// 				 pImageResult->result->in_shareImage->strCameraName, pImageResult->result->in_shareImage->strChannel);
 
 	// 简单硬拼接：img 在左，img_B 在右，拼接结果写回 img
-	// 水平方向有overlap，拼接时固定去掉左图最右边overlap区域
+	// 先去掉左图最右边overlap区域，再做首行判断与上下对齐，最后水平拼接
 	if (pImageResult->result->in_shareImage->img.size() != pImageResult->result->in_shareImage->img_B.size() ||
 		pImageResult->result->in_shareImage->img.type() != pImageResult->result->in_shareImage->img_B.type())
 	{
@@ -178,50 +179,79 @@ int CameraCheckAnalysisy::Detect_Pre()
 	cv::Mat &img = pImageResult->result->in_shareImage->img;
 	cv::Mat &img_B = pImageResult->result->in_shareImage->img_B;
 
-	// 左右图存在上下错位: 找到左右图产品所在的第一行, 进行简单上下对齐
+	// 1、先裁剪左图: 固定去掉左图最右侧与右图重叠的 overlap 区域
+	int overlap = cvRound(m_pbaseCheckFunction->markLine.hoverlap);
+	if (img.cols <= overlap)
 	{
-		int rowLeft = -1;
-		int rowRight = -1;
-		if (GetProductFirstRow(img, rowLeft) == 0 && GetProductFirstRow(img_B, rowRight) == 0)
+		m_pdetlog->AddCheckstr(PrintLevel_1, "Detect_Pre", "Cam   %s ==================img.cols <= overlap %d ", strBasic.c_str(), overlap);
+		return 1;
+	}
+	img = img(cv::Rect(0, 0, img.cols - overlap, img.rows));
+
+	// 拼接缝: 左图右边界在拼接图中的 x
+	const int seamX = img.cols;
+
+	// 2、裁剪后做顶边对齐: 分别拟合左右图产品顶边直线, 用直线在拼接缝处的行坐标做上下平移。
+	//    只用单一坐标值(首行)相减误差大, 产品倾斜时会拼接不准; 用拟合出的直线取值更稳定。
+	//    这里只做上下平移, 不旋转图像。
+	bool bFitOK = false;	// 顶边拟合是否成功
+	cv::Point2f pL1, pL2;	// 左图顶边线段(端点)
+	cv::Point2f pR1, pR2;	// 右图顶边线段(端点)
+	int dyAlign = 0;		// 右图的上下平移量(>0 向下, <0 向上)
+	{
+		float kL = 0.0f, bL = 0.0f; // 左图顶边直线: y = kL * x + bL
+		float kR = 0.0f, bR = 0.0f; // 右图顶边直线: y = kR * x + bR
+
+		int reL = FitProductTopLine(img, kL, bL, pL1, pL2);
+		int reR = FitProductTopLine(img_B, kR, bR, pR1, pR2);
+
+		if (reL == 0 && reR == 0)
 		{
-			// 以较高的顶边为基准, 把较低的那张图向上平移, 使两图顶边对齐
-			int refRow = std::min(rowLeft, rowRight);
-			int shiftLeft = refRow - rowLeft;	// <= 0, 负值表示向上平移
-			int shiftRight = refRow - rowRight; // <= 0
+			// 左图顶边直线在拼接缝处(x = seamX)的行坐标 = 左图线段右端点的高度
+			const double seamY = kL * seamX + bL;
+			// 右图顶边直线在其左边界(x = 0)处的行坐标 = 右图线段左端点的高度
+			const double rightY = bR;
 
-			m_pdetlog->AddCheckstr(PrintLevel_1, "Detect_Pre", "Cam   %s ==================align rowLeft %d rowRight %d shiftLeft %d shiftRight %d",
-								   strBasic.c_str(), rowLeft, rowRight, shiftLeft, shiftRight);
+			// 让两条线段的连接端点在拼接缝处相接: 右图上下平移 seamY - rightY 行
+			dyAlign = (int)cvRound(seamY - rightY);
 
-			if (shiftLeft < 0)
-			{
-				// 内容向上平移 |shiftLeft| 行，底部补黑。
-				int dy = -shiftLeft;
-				cv::Mat shifted = cv::Mat::zeros(img.size(), img.type());
-				img(cv::Rect(0, dy, img.cols, img.rows - dy)).copyTo(shifted(cv::Rect(0, 0, img.cols, img.rows - dy)));
-				img = shifted;
-			}
-			if (shiftRight < 0)
+			m_pdetlog->AddCheckstr(PrintLevel_1, "Detect_Pre",
+								   "Cam   %s ==================topline L(k %.5f b %.1f) R(k %.5f b %.1f) seamY %.1f rightY %.1f dy %d",
+								   strBasic.c_str(), kL, bL, kR, bR, seamY, rightY, dyAlign);
+
+			// 上下平移右图, 空出的部分补黑
+			if (dyAlign != 0 && dyAlign > -img_B.rows && dyAlign < img_B.rows)
 			{
-				int dy = -shiftRight;
 				cv::Mat shifted = cv::Mat::zeros(img_B.size(), img_B.type());
-				img_B(cv::Rect(0, dy, img_B.cols, img_B.rows - dy)).copyTo(shifted(cv::Rect(0, 0, img_B.cols, img_B.rows - dy)));
+				if (dyAlign > 0)
+				{
+					// 内容向下平移 dyAlign 行, 顶部补黑
+					img_B(cv::Rect(0, 0, img_B.cols, img_B.rows - dyAlign))
+						.copyTo(shifted(cv::Rect(0, dyAlign, img_B.cols, img_B.rows - dyAlign)));
+				}
+				else
+				{
+					// 内容向上平移 |dyAlign| 行, 底部补黑
+					const int d = -dyAlign;
+					img_B(cv::Rect(0, d, img_B.cols, img_B.rows - d))
+						.copyTo(shifted(cv::Rect(0, 0, img_B.cols, img_B.rows - d)));
+				}
 				img_B = shifted;
 			}
+
+			bFitOK = true;
 		}
 		else
 		{
-			m_pdetlog->AddCheckstr(PrintLevel_1, "Detect_Pre", "Cam   %s ==================GetProductFirstRow fail, skip align", strBasic.c_str());
+			m_pdetlog->AddCheckstr(PrintLevel_1, "Detect_Pre", "Cam   %s ==================FitProductTopLine fail(L %d R %d), skip align", strBasic.c_str(), reL, reR);
 		}
 	}
-	int overlap = cvRound(m_pbaseCheckFunction->markLine.hoverlap);
-	if (img.cols <= overlap)
+	// 3、上下对齐后水平拼接
 	{
-		m_pdetlog->AddCheckstr(PrintLevel_1, "Detect_Pre", "Cam   %s ==================img.cols <= overlap %d ", strBasic.c_str(), overlap);
-		return 1;
+		cv::Mat merge_img;
+		cv::hconcat(img, img_B, merge_img);
+		img = merge_img;
 	}
-	cv::Mat merge_img;
-	cv::hconcat(img(cv::Rect(0, 0, img.cols - overlap, img.rows)), img_B, merge_img);
-	img = merge_img;
 
 	// 拼接完成后 img_B 不再需要，立即释放内存
 	img_B.release();
@@ -229,6 +259,45 @@ int CameraCheckAnalysisy::Detect_Pre()
 	if (pImageResult->result->in_shareImage->Det_Mode == DET_MODE_MergeImg)
 	{
 		cv::imwrite(pImageResult->result->in_shareImage->strCameraName + "_MergeImg.jpg", pImageResult->result->in_shareImage->img);
+
+		// 保存绘制了顶边线段的图(绿: 左图顶边线段, 红: 右图顶边线段, 黄/青点: 两条线段的连接端点)
+		if (bFitOK)
+		{
+			// 先缩放到可读尺寸再画线, 避免在超大图上做彩色转换(内存/耗时)
+			const cv::Mat &mergeImg = pImageResult->result->in_shareImage->img;
+			const int dbgW = 4096;
+			const double sc = std::min(1.0, (double)dbgW / mergeImg.cols);
+			cv::Mat small;
+			cv::resize(mergeImg, small, cv::Size(), sc, sc, cv::INTER_AREA);
+
+			cv::Mat dbgLineImg;
+			if (small.channels() == 1)
+			{
+				cv::cvtColor(small, dbgLineImg, cv::COLOR_GRAY2BGR);
+			}
+			else
+			{
+				dbgLineImg = small.clone();
+			}
+
+			// 左图顶边线段(拼接图左侧)
+			const cv::Point2f a1((float)(pL1.x * sc), (float)(pL1.y * sc));
+			const cv::Point2f a2((float)(pL2.x * sc), (float)(pL2.y * sc));
+			// 右图顶边线段(拼接图右侧, 并叠加本次的上下平移量)
+			const cv::Point2f b1((float)((seamX + pR1.x) * sc), (float)((pR1.y + dyAlign) * sc));
+			const cv::Point2f b2((float)((seamX + pR2.x) * sc), (float)((pR2.y + dyAlign) * sc));
+
+			const int thick = std::max(2, (int)cvRound(9 * sc));
+			cv::line(dbgLineImg, a1, a2, cv::Scalar(0, 255, 0), thick); // 绿: 左图顶边线段
+			cv::line(dbgLineImg, b1, b2, cv::Scalar(0, 0, 255), thick); // 红: 右图顶边线段
+
+			// 两条线段的连接端点(平移对齐后应重合在拼接缝处)
+			const int r = std::max(4, (int)cvRound(14 * sc));
+			cv::circle(dbgLineImg, a2, r, cv::Scalar(0, 255, 255), -1);
+			cv::circle(dbgLineImg, b1, r, cv::Scalar(255, 255, 0), -1);
+
+			cv::imwrite(pImageResult->result->in_shareImage->strCameraName + "_MergeImg_Line.jpg", dbgLineImg);
+		}
 		return 2;
 	}
 
@@ -1633,3 +1702,168 @@ int CameraCheckAnalysisy::GetProductFirstRow(const cv::Mat &img, int &outRow)
 
 	return 2; // 理论上不会走到这里
 }
+
+// 拟合图像中产品(亮色区域)的顶边直线段: 对顶边上的点做最小二乘拟合, 得到 y = k * x + b
+// 产品倾斜时顶边在每一列的高度不同, 用逐列顶边点拟合出的直线才能真正反映倾斜,
+// 左右两张图各拟合一条线段后, 就可以让它们在拼接缝处首尾相连。
+// 返回 0 成功; 非 0 失败(未找到顶边或有效点太少)
+int CameraCheckAnalysisy::FitProductTopLine(const cv::Mat &img, float &outK, float &outB,
+										   cv::Point2f &outP1, cv::Point2f &outP2)
+{
+	outK = 0.0f;
+	outB = 0.0f;
+	outP1 = cv::Point2f(0.0f, 0.0f);
+	outP2 = cv::Point2f(0.0f, 0.0f);
+
+	if (img.empty())
+	{
+		return 1;
+	}
+
+	cv::Mat gray;
+	if (img.channels() == 1)
+	{
+		gray = img;
+	}
+	else if (img.channels() == 3)
+	{
+		cv::cvtColor(img, gray, cv::COLOR_BGR2GRAY);
+	}
+	else
+	{
+		return 1;
+	}
+
+	const uchar brightThreshold = 45;								// 亮像素灰度阈值(与背景判断阈值保持一致)
+	const int needBrightCount = std::max(1, (int)(gray.cols * 0.1)); // 该行亮像素数量占比超过 20% 认为是产品所在行
+	const int rowStep = 16;													// 跳行扫描步长
+
+	// 判断某一行是否为产品所在行(亮像素数量达到阈值)
+	auto isProductRow = [&](int row) -> bool
+	{
+		const uchar *p = gray.ptr<uchar>(row);
+		int brightCount = 0;
+		for (int col = 0; col < gray.cols; ++col)
+		{
+			if (p[col] >= brightThreshold)
+			{
+				if (++brightCount >= needBrightCount)
+				{
+					return true;
+				}
+			}
+		}
+		return false;
+	};
+
+	// 1、跳行粗扫: 定位产品顶边的大致位置
+	int rowFound = -1;
+	for (int row = 0; row < gray.rows; row += rowStep)
+	{
+		if (isProductRow(row))
+		{
+			rowFound = row;
+			break;
+		}
+	}
+
+	if (rowFound < 0)
+	{
+		return 2; // 未找到产品顶边
+	}
+
+	// 2、逐列定位顶边点: 在粗定位行附近的范围内, 从上往下找到第一段连续亮像素
+	//    产品倾斜时顶边在每列的行坐标不同, 这些点连起来才是真实的顶边线段
+	const int yMargin = std::max(rowStep * 4, 60);		// 纵向搜索范围(要能覆盖倾斜造成的高度差)
+	const int yStart = std::max(0, rowFound - yMargin);
+	const int yEnd = std::min(gray.rows - 1, rowFound + yMargin);
+	const int colStep = std::max(1, gray.cols / 200);	// 采样列步长, 避免逐列遍历超大图
+	const int minRunLen = 3;							// 连续亮像素个数, 用于过滤噪点
+
+	std::vector<cv::Point2f> points;
+	points.reserve(gray.cols / colStep + 1);
+	for (int col = 0; col < gray.cols; col += colStep)
+	{
+		for (int row = yStart; row <= yEnd; ++row)
+		{
+			int runLen = 0;
+			for (int k = 0; k < minRunLen && row + k <= yEnd; ++k)
+			{
+				if (gray.at<uchar>(row + k, col) >= brightThreshold)
+				{
+					runLen++;
+				}
+				else
+				{
+					break;
+				}
+			}
+			if (runLen >= minRunLen)
+			{
+				points.push_back(cv::Point2f((float)col, (float)row));
+				break;
+			}
+		}
+	}
+
+	if (points.size() < 10)
+	{
+		return 3; // 顶边点太少, 无法拟合
+	}
+
+	// 3、迭代最小二乘拟合 y = k * x + b, 并用残差剔除离群点
+	std::vector<cv::Point2f> fitPts = points;
+	float k = 0.0f;
+	float b = 0.0f;
+	for (int iter = 0; iter < 3; ++iter)
+	{
+		const int n = (int)fitPts.size();
+		if (n < 2)
+		{
+			break;
+		}
+		double sx = 0.0, sy = 0.0, sxx = 0.0, sxy = 0.0;
+		for (const auto &p : fitPts)
+		{
+			sx += p.x;
+			sy += p.y;
+			sxx += (double)p.x * p.x;
+			sxy += (double)p.x * p.y;
+		}
+		const double denom = n * sxx - sx * sx;
+		if (std::abs(denom) < 1e-6)
+		{
+			k = 0.0f; // 所有点几乎在同一列, 无法确定斜率
+			b = (float)(sy / n);
+		}
+		else
+		{
+			k = (float)((n * sxy - sx * sy) / denom);
+			b = (float)((sy - k * sx) / n);
+		}
+
+		// 残差剔除: 偏离拟合直线过多的点不参与下一次拟合
+		std::vector<cv::Point2f> inliers;
+		inliers.reserve(fitPts.size());
+		for (const auto &p : fitPts)
+		{
+			if (std::abs(p.y - (k * p.x + b)) <= 3.0f)
+			{
+				inliers.push_back(p);
+			}
+		}
+		if (inliers.size() == fitPts.size() || inliers.size() < 10)
+		{
+			break;
+		}
+		fitPts.swap(inliers);
+	}
+
+	outK = k;
+	outB = b;
+	// 线段端点: 取图像左右边界上的点
+	outP1 = cv::Point2f(0.0f, b);
+	outP2 = cv::Point2f((float)(gray.cols - 1), k * (gray.cols - 1) + b);
+
+	return 0;
+}
diff --git a/TcsCheckModule/src/TcsCheck.cpp b/TcsCheckModule/src/TcsCheck.cpp
index 639656a..50d1276 100644
--- a/TcsCheckModule/src/TcsCheck.cpp
+++ b/TcsCheckModule/src/TcsCheck.cpp
@@ -204,7 +204,7 @@ cv::Mat CTcsCheck::AdaptiveBinary(cv::Mat matBlur)
 			if (m_cpCfg.bDebugsaveImg)
 			{
 				std::string roi_ext = "[" + std::to_string(x) + "," + std::to_string(y) + "]";
-				std::string strSaveDir = "/home/aidlux/BOE/CELL_AOI/AI_Detect/" + m_cpCfg.productId + "/";
+				std::string strSaveDir = "/home/aidlux/BOE/CELL_AOI/Tradition_Detect/" + m_cpCfg.productId + "/";
 				MakeDirs(strSaveDir);
 				std::string strSavePath_in = strSaveDir + m_cpCfg.productChannel +"_"+ roi_ext + "_"+ "in" + ".png";
 				std::string strSavePath_out = strSaveDir + m_cpCfg.productChannel +"_"+ roi_ext + "_"+ "out" + ".png";