diff --git a/app/AIProcessor.py b/app/AIProcessor.py
index 3c91781..45902c4 100644
--- a/app/AIProcessor.py
+++ b/app/AIProcessor.py
@@ -22,6 +22,7 @@ def __init__(self, yolo_path: str, sam_path: str, device: torch.device = torch.d
         self.size = 0
         self.height = 0
         self.width = 0
+        self.upper_rgb = (40, 40, 45)
 
     def remove_points_in_bboxes(self, point_list, label_list, bbox_list):
         def is_point_in_bbox(p, b):
@@ -60,7 +61,7 @@ def object_detection(self, img):
                                           iou=0.3, conf=0.3)
         bbox = results[0].boxes.xyxy.tolist()
         self.indices = [index for index, value in enumerate(results[0].boxes.cls) if value == 1.0]
-        logging.info(f'객체 탐지 - {self.indices} 박스에 동그라미 존재')
+        logging.info(f'객체 탐지 - {len(bbox)}개 결과 중, 동그란 마킹 {len(self.indices)}개 ({self.indices})')
         return bbox
 
     def segment_from_yolo(self, image, bbox, save_path=None):
@@ -107,7 +108,7 @@ def inpaint_from_yolo(self, image, bbox=None):
             masks_np[miny:maxy, minx:maxx] = 255  # 박스 영역을 255로 채움
 
             roi = image[miny:maxy, minx:maxx]  # 박스 내 복사하고 텍스트만 추출하기
-            roi_mask = cv2.inRange(roi, (0, 0, 0), (40, 40, 45))  # roi 내에서 인쇄된 글씨는 255값
+            roi_mask = cv2.inRange(roi, (0, 0, 0), self.upper_rgb)  # roi 내에서 인쇄된 글씨는 255값
             text_np[miny:maxy, minx:maxx] = roi_mask
 
             if maxy < image.shape[0]-2:  # 박스 근처 BG 컬러 샘플링
@@ -148,7 +149,7 @@ def combine_alpha(self, image_rgb):
         else:
             return image_rgb
 
-    def process(self, img_bytes, user_inputs, extension='jpg'):
+    def process(self, img_bytes, user_inputs, user_intensity, extension='jpg'):
         """ local test 용도 Vs. server test 용도 구분 """
         ### local 용도
         # img_path = img_bytes
@@ -163,6 +164,9 @@ def process(self, img_bytes, user_inputs, extension='jpg'):
         ### ready
         #self.load_sam_model()
         #self.predictor.set_image(image)
+        if user_intensity is not None and len(user_intensity) == 3:
+            self.upper_rgb = (user_intensity[0], user_intensity[1], user_intensity[2])
+        logging.info(f"인식할 text 색상 범위: (0, 0, 0) ~ {self.upper_rgb}")
         masks_total = np.zeros(image.shape[:2], dtype=np.uint8)
 
         ### 1차: Segment by Object Detection
diff --git a/app/main.py b/app/main.py
index 3dfd2f9..9b46834 100644
--- a/app/main.py
+++ b/app/main.py
@@ -112,9 +112,9 @@ async def processShape(request: Request):
     data = await request.json()
     full_url = data['fullUrl']
     point_list = data.get('points')
-    label_list = data.get('labels')  # value or None
+    intensity = data.get('intensity')  # value or None
     logger.info(f"사용자 입력 포인트: {point_list}")
-    logger.info(f"사용자 입력 라벨: {label_list}")
+    logger.info(f"사용자 입력 강도: {intensity}")
 
     try:
         s3_key = parse_s3_url(full_url)
@@ -126,7 +126,8 @@ async def processShape(request: Request):
         # aiProcessor = AIProcessor(yolo_path='/Users/semin/models/yolo11_best.pt', sam_path='/Users/semin/models/mobile_sam.pt')  # local
         aiProcessor = AIProcessor(yolo_path="../models/yolo11_best.pt", sam_path="../models/mobile_sam.pt")  # server
         img_input_bytes, img_mask_bytes, img_output_bytes, one, two = aiProcessor.process(img_bytes=corrected_img_bytes,
-                                                                                          user_inputs=point_list)
+                                                                                          user_inputs=point_list,
+                                                                                          user_intensity=intensity)
         logger.info("AI 필기 제거 프로세스 완료")
 
         upload_image_to_s3(img_input_bytes, paths["input_path"])