fix: YOLO TensorRT 输出解析修复

- TensorRT 输出 shape: (1, 84, 4725)  (84, 4725)
- 正确解析 YOLO 输出格式: boxes[0:4], obj_conf[4], cls_scores[5:]
- 移除错误的 detection 遍历逻辑
- 工业级向量化操作代替 Python 循环

core/postprocessor.py

@@ -600,54 +600,44 @@ class PostProcessor:
         if output.ndim == 3:
             output = output[0]
         
-        if output.ndim == 2:
-            output = output.reshape(-1)
-        
-        num_detections = output.shape[0] // 85
-        
-        boxes = []
-        scores = []
-        class_ids = []
-        
-        for i in range(num_detections):
-            start_idx = i * 85
-            detection = output[start_idx:start_idx + 85]
-            
-            x_center = detection[0]
-            y_center = detection[1]
-            width = detection[2]
-            height = detection[3]
-            
-            obj_conf = detection[4]
-            
-            class_scores = detection[5:]
-            if len(class_scores) == 0:
-                continue
-            
-            class_id = np.argmax(class_scores)
-            class_conf = class_scores[class_id]
-            
-            total_conf = obj_conf * class_conf
-            
-            if total_conf < 0.0:
-                continue
-            
-            x1 = x_center - width / 2
-            y1 = y_center - height / 2
-            x2 = x_center + width / 2
-            y2 = y_center + height / 2
-            
-            boxes.append([x1, y1, x2, y2])
-            scores.append(float(total_conf))
-            class_ids.append(int(class_id))
-        
-        if not boxes:
+        if output.ndim != 2:
             return np.array([]), np.array([]), np.array([])
         
+        if output.shape[0] != 84:
+            return np.array([]), np.array([]), np.array([])
+        
+        num_boxes = output.shape[1]
+        
+        boxes_xywh = output[0:4, :].T
+        
+        obj_conf = output[4, :]
+        
+        cls_scores = output[5:, :]
+        
+        cls_ids = np.argmax(cls_scores, axis=0)
+        cls_conf = cls_scores[cls_ids, np.arange(num_boxes)]
+        
+        scores = obj_conf * cls_conf
+        
+        valid_mask = scores > self._conf_threshold
+        
+        if not np.any(valid_mask):
+            return np.array([]), np.array([]), np.array([])
+        
+        boxes = boxes_xywh[valid_mask]
+        scores = scores[valid_mask]
+        class_ids = cls_ids[valid_mask]
+        
+        boxes_xyxy = np.zeros_like(boxes)
+        boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2
+        boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2
+        boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2
+        boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2
+        
         return (
-            np.array(boxes, dtype=np.float32),
-            np.array(scores, dtype=np.float32),
-            np.array(class_ids, dtype=np.int32)
+            boxes_xyxy.astype(np.float32),
+            scores.astype(np.float32),
+            class_ids.astype(np.int32)
         )
     
     def filter_by_roi(