diff --git a/infer_camera_direct.py b/infer_camera_direct.py
new file mode 100644
index 0000000..26306f8
--- /dev/null
+++ b/infer_camera_direct.py
@@ -0,0 +1,158 @@
+import os
+import cv2
+import numpy as np
+import time
+from ais_bench.infer.interface import InferSession
+
+# ==================== 硬编码配置区域 ====================
+MODEL_PATH = "./yolo26_8t.om"  # 你的 YOLO26 模型路径
+CONF_THRESHOLD = 0.25  # 置信度过滤阈值 (YOLO26 推荐 0.25)
+CAMERA_INDEX = 0  # 摄像头索引 (默认0，如果打不开可以尝试1或2)
+
+
+# ========================================================
+
+class YOLO26CameraInfer:
+    def __init__(self, model_path, device_id=0):
+        self.device_id = device_id
+        self.model_path = model_path
+
+        # 自动检查模型文件是否存在，防止硬编码路径写错导致程序崩溃
+        if not os.path.exists(model_path):
+            raise FileNotFoundError(f"找不到硬编码的模型文件: {model_path}，请检查路径是否正确。")
+
+        # 创建 ais_bench 推理会话
+        self.session = InferSession(device_id, model_path)
+        self.input_width = 640
+        self.input_height = 640
+
+    def letterbox(self, img, new_shape=(640, 640), color=(114, 114, 114)):
+        shape = img.shape[:2]
+        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+        new_unpad = (int(round(shape[1] * r)), int(round(shape[0] * r)))
+        dw = new_shape[1] - new_unpad[0]
+        dh = new_shape[0] - new_unpad[1]
+        left = dw // 2
+        right = dw - left
+        top = dh // 2
+        bottom = dh - top
+
+        if shape[::-1] != new_unpad:
+            img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+
+        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
+        return img, r, (left, top)
+
+    def preprocess_frame(self, img_bgr):
+        self.original_height, self.original_width = img_bgr.shape[:2]
+        img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+        img_resized, self.ratio, (self.pad_left, self.pad_top) = self.letterbox(
+            img_rgb, (self.input_width, self.input_height)
+        )
+        img_input = (img_resized.astype(np.float32) / 255.0).astype(np.float32)
+        img_input = np.ascontiguousarray(np.transpose(img_input, (2, 0, 1))[np.newaxis, :, :, :])
+        return img_input
+
+    def infer(self, img_input):
+        return self.session.infer([img_input])
+
+    def postprocess(self, infer_result, conf_threshold):
+        detections = []
+        if isinstance(infer_result, (list, tuple)):
+            output = infer_result[0]
+        else:
+            output = infer_result
+
+        if output.ndim == 3:
+            output = output[0]
+
+        for det in output:
+            confidence = det[4]
+            if confidence < conf_threshold:
+                continue
+
+            x1_scale, y1_scale, x2_scale, y2_scale = det[0], det[1], det[2], det[3]
+            cls_id = int(det[5])
+
+            x1 = (x1_scale - self.pad_left) / self.ratio
+            y1 = (y1_scale - self.pad_top) / self.ratio
+            x2 = (x2_scale - self.pad_left) / self.ratio
+            y2 = (y2_scale - self.pad_top) / self.ratio
+
+            x1 = max(0, min(x1, self.original_width))
+            y1 = max(0, min(y1, self.original_height))
+            x2 = max(0, min(x2, self.original_width))
+            y2 = max(0, min(y2, self.original_height))
+
+            detections.append([x1, y1, x2, y2, confidence, cls_id])
+
+        return detections
+
+
+def draw_boxes(image, detections):
+    for det in detections:
+        x1, y1, x2, y2, conf, cls_id = det
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+
+        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        label = f"Class: {cls_id} {conf:.2f}"
+
+        (label_w, label_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+        cv2.rectangle(image, (x1, y1 - label_h - 5), (x1 + label_w, y1), (0, 255, 0), -1)
+        cv2.putText(image, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
+
+    return image
+
+
+if __name__ == "__main__":
+    print("正在初始化 YOLOv26 离线推理引擎...")
+    # 传入硬编码的模型路径
+    yolo_infer = YOLO26CameraInfer(MODEL_PATH)
+
+    # 打开硬编码指定的摄像头
+    cap = cv2.VideoCapture(CAMERA_INDEX)
+    if not cap.isOpened():
+        print(f"错误：无法打开摄像头索引 {CAMERA_INDEX}，请检查物理连接。")
+        exit()
+
+    # 尝试设置合适的分辨率
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
+
+    print(f"初始化成功！当前模型：{MODEL_PATH} | 过滤阈值：{CONF_THRESHOLD}")
+    print("开始实时检测，在画面窗口按下 'q' 键可退出...")
+
+    fps_start_time = 0
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            continue
+
+        # 预处理与 NPU 推理
+        input_tensor = yolo_infer.preprocess_frame(frame)
+        raw_result = yolo_infer.infer(input_tensor)
+
+        # 后处理（使用硬编码的置信度）
+        detections = yolo_infer.postprocess(raw_result, conf_threshold=CONF_THRESHOLD)
+
+        # 绘制结果
+        frame = draw_boxes(frame, detections)
+
+        # FPS 实时计算与显示
+        fps_end_time = time.time()
+        time_diff = fps_end_time - fps_start_time
+        fps = 1 / time_diff if time_diff > 0 else 0
+        fps_start_time = fps_end_time
+
+        cv2.putText(frame, f"FPS: {fps:.1f}", (20, 40),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
+
+        cv2.imshow("YOLOv26 Real-time Detection", frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+    print("检测已结束。")
\ No newline at end of file
diff --git a/mav-test.py b/mav-test.py
index 1c570e4..0795ed7 100644
--- a/mav-test.py
+++ b/mav-test.py
@@ -164,7 +164,7 @@ class DroneDetector:
 
 
 if __name__ == "__main__":
-    MODEL_PATH = "./om/yolo26n.om"
+    MODEL_PATH = "./om/yolo26n-drone.om"
     SERIAL_PORT = "/dev/ttyAMA0"  # 根据实际修改
     BAUD = 57600
 
diff --git a/om/yolo26n.om b/om/yolo26n-balloon.om
similarity index 52%
rename from om/yolo26n.om
rename to om/yolo26n-balloon.om
index 38c6c40..8676a10 100644
Binary files a/om/yolo26n.om and b/om/yolo26n-balloon.om differ
diff --git a/om/yolov8n.om b/om/yolov8n.om
deleted file mode 100644
index 7264694..0000000
Binary files a/om/yolov8n.om and /dev/null differ