diff --git a/laser_detector.py b/laser_detector.py
index 8e9a6db..24530fb 100644
--- a/laser_detector.py
+++ b/laser_detector.py
@@ -14,12 +14,16 @@ except ImportError:
 WIDTH = 640
 HEIGHT = 480
 THRESHOLD = 100
-RED_RATIO = 1
-SEARCH_RADIUS = 60
+RED_RATIO = 1.5
+SEARCH_RADIUS = 80
+TRACK_RADIUS = 30
+MIN_PIXELS = 3
+COARSE_STEP = 2
 STABLE_COUNT = 2
+MAX_SKIP_FRAMES = 5
 
 # Temporal smoothing
-_EMA_ALPHA = 0.4
+_EMA_ALPHA = 0.35
 _GATE_PX = 10
 _FRAME_INTERVAL_MS = 50
 
@@ -83,44 +87,74 @@ def find_ellipse(img_cv, cx, cy, roi_r, th, ratio):
     return None
 
 
+def is_red(r, g, b, th, ratio):
+    if r > th and r > g * ratio and r > b * ratio:
+        return True
+    if (r > 200 and g > 200 and b > 200 and r >= g and r >= b
+            and (r - g) > 10 and (r - b) > 10):
+        return True
+    return False
+
+
 def find_brightest_bytes(frame, cx, cy, roi_r, th, ratio):
     x1 = max(0, cx - roi_r)
     x2 = min(WIDTH, cx + roi_r)
     y1 = max(0, cy - roi_r)
     y2 = min(HEIGHT, cy + roi_r)
     data = frame.to_bytes()
-    rs, gs, bs = [], [], []
-    xs, ys = [], []
-    step = 2
-    for y in range(y1, y2, step):
-        for x in range(x1, x2, step):
+
+    best_score = 0
+    best_x = (x1 + x2) // 2
+    best_y = (y1 + y2) // 2
+    found_any = False
+    for y in range(y1, y2, COARSE_STEP):
+        for x in range(x1, x2, COARSE_STEP):
             idx = (y * WIDTH + x) * 3
             r = data[idx]
             g = data[idx + 1]
             b = data[idx + 2]
-            if (r > th and r > g * ratio and r > b * ratio) or \
-                    (r > 200 and g > 200 and b > 200 and r >= g and r >= b and (r - g) > 10 and (r - b) > 10):
-                rs.append(r)
-                gs.append(g)
-                bs.append(b)
-                xs.append(x)
-                ys.append(y)
-    if not rs:
+            if is_red(r, g, b, th, ratio):
+                score = r + g + b
+                dx = x - cx
+                dy = y - cy
+                dist_decay = max(0.5, 1.0 - ((dx * dx + dy * dy) ** 0.5 / roi_r) * 0.5)
+                score *= dist_decay
+                if score > best_score:
+                    best_score = score
+                    best_x = x
+                    best_y = y
+                    found_any = True
+
+    if not found_any:
         return None
-    rs = np.array(rs, dtype=np.float64)
-    gs = np.array(gs, dtype=np.float64)
-    bs = np.array(bs, dtype=np.float64)
-    xs = np.array(xs, dtype=np.float64)
-    ys = np.array(ys, dtype=np.float64)
-    w = rs - np.maximum(gs, bs)
-    w = np.clip(w, 0, None)
-    w = w * w
-    total_w = w.sum()
-    if total_w < 1e-6:
-        return None
-    cx_f = (xs * w).sum() / total_w
-    cy_f = (ys * w).sum() / total_w
-    return (float(cx_f), float(cy_f))
+
+    sf = 4
+    fx1 = max(x1, best_x - sf)
+    fx2 = min(x2, best_x + sf + 1)
+    fy1 = max(y1, best_y - sf)
+    fy2 = min(y2, best_y + sf + 1)
+
+    sum_x = 0.0
+    sum_y = 0.0
+    total_w = 0.0
+    count = 0
+    for y in range(fy1, fy2):
+        for x in range(fx1, fx2):
+            idx = (y * WIDTH + x) * 3
+            r = data[idx]
+            g = data[idx + 1]
+            b = data[idx + 2]
+            if is_red(r, g, b, th, ratio):
+                w = r + g + b
+                sum_x += x * w
+                sum_y += y * w
+                total_w += w
+                count += 1
+
+    if count < MIN_PIXELS:
+        return (float(best_x), float(best_y))
+
+    return (float(sum_x / total_w), float(sum_y / total_w))
 
 
 def _ema_filter(pos, alpha=_EMA_ALPHA):
@@ -151,6 +185,8 @@ def get_stable_laser_point(timeout_ms=15000, stable_count=STABLE_COUNT):
         stable = 0
         start = time.ticks_ms()
         cx, cy = WIDTH // 2, HEIGHT // 2
+        track_count = 0
+        skip_count = 0
         while True:
             if abs(time.ticks_diff(time.ticks_ms(), start)) > timeout_ms:
                 _prev_smoothed = None
@@ -159,19 +195,27 @@ def get_stable_laser_point(timeout_ms=15000, stable_count=STABLE_COUNT):
             if frame is None:
                 time.sleep_ms(10)
                 continue
-            pos_bright = find_brightest_bytes(frame, cx, cy, SEARCH_RADIUS, THRESHOLD, RED_RATIO)
+
+            if track_count > 0 and _prev_smoothed is not None:
+                search_cx = int(_prev_smoothed[0])
+                search_cy = int(_prev_smoothed[1])
+                search_r = TRACK_RADIUS
+            else:
+                search_cx = cx
+                search_cy = cy
+                search_r = SEARCH_RADIUS
+
+            pos_bright = find_brightest_bytes(frame, search_cx, search_cy, search_r, THRESHOLD, RED_RATIO)
             pos = pos_bright
             if _USE_CV:
                 img_cv = image.image2cv(frame, False, False)
-                pos_ellipse = find_ellipse(img_cv, cx, cy, SEARCH_RADIUS, THRESHOLD, RED_RATIO)
+                pos_ellipse = find_ellipse(img_cv, search_cx, search_cy, search_r, THRESHOLD, RED_RATIO)
                 if pos_ellipse is not None:
                     pos = pos_ellipse
+
             if pos is not None:
-                if not _gated(pos):
-                    if logger_manager.logger:
-                        logger_manager.logger.info(f"pos:{pos} gated,stable:{stable}")
-                    time.sleep_ms(_FRAME_INTERVAL_MS)
-                    continue
+                skip_count = 0
+                track_count += 1
                 filtered = _ema_filter(pos)
                 if last_raw is not None:
                     dx = abs(filtered[0] - last_raw[0])
@@ -189,6 +233,16 @@ def get_stable_laser_point(timeout_ms=15000, stable_count=STABLE_COUNT):
                     result = (int(filtered[0]), int(filtered[1]))
                     _prev_smoothed = None
                     return result
+            else:
+                skip_count += 1
+                if logger_manager.logger:
+                    logger_manager.logger.info(f"find_brightest_bytes None, skip={skip_count}, track={track_count}, search_center=({search_cx},{search_cy}), search_r={search_r}")
+                if skip_count > MAX_SKIP_FRAMES:
+                    _prev_smoothed = None
+                    track_count = 0
+                    stable = 0
+                    last_raw = None
+
             time.sleep_ms(_FRAME_INTERVAL_MS)
     finally:
         _prev_smoothed = None