refactor: add YOLOConfig for centralized configuration management

Improvements:
- Create YOLOConfig data class with all inference settings consolidated
- Add CoordinateMode and PreprocessingTechnique enums for type safety
- Update YOLOInferenceEngine constructor to accept configurable settings
- Replace scattered constants with config usage in key methods
- Enable runtime configuration of model file, thresholds, and processing options

Maintainability Benefits:
- All YOLO settings in one place for easy adjustment
- Type-safe enums prevent configuration errors
- Default values maintain backward compatibility
- Cleaner separation between configuration and implementation

Performance Impact:
- Thread pool size now configurable via config.threadPoolSize
- Confidence and processing thresholds easily adjustable
- Foundation for A/B testing different configurations

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

app/src/main/java/com/quillstudios/pokegoalshelper/ml/YOLOConfig.kt

@@ -0,0 +1,67 @@
+package com.quillstudios.pokegoalshelper.ml
+
+/**
+ * Configuration class for YOLO inference engine
+ * Consolidates all settings in one place for better maintainability
+ */
+data class YOLOConfig(
+    // Model settings
+    val modelFile: String = "best.onnx",
+    val inputSize: Int = 640,
+    val numChannels: Int = 3,
+    val numDetections: Int = 300,
+    val numClasses: Int = 95,
+    
+    // Confidence and NMS thresholds
+    val confidenceThreshold: Float = 0.55f,
+    val nmsThreshold: Float = 0.3f,
+    val iouThreshold: Float = 0.4f,
+    
+    // Preprocessing settings
+    val enableMultiPreprocessing: Boolean = false, // Disabled for mobile performance
+    val enableTTA: Boolean = true, // Test-time augmentation
+    val enableUltralyticsPreprocessing: Boolean = true,
+    val enableContrastEnhancement: Boolean = true,
+    val enableSharpening: Boolean = true,
+    val enableNoiseReduction: Boolean = true,
+    
+    // Performance settings
+    val maxInferenceTimeMs: Long = 4500L, // Leave 500ms for other processing
+    val threadPoolSize: Int = 3,
+    
+    // Coordinate transformation mode
+    val coordinateMode: CoordinateMode = CoordinateMode.HYBRID,
+    
+    // Debug settings
+    val showAllConfidences: Boolean = false,
+    val classFilter: String? = null
+)
+
+/**
+ * Coordinate transformation modes
+ */
+enum class CoordinateMode(val modeName: String)
+{
+    HYBRID("HYBRID"),
+    LETTERBOX("LETTERBOX"), 
+    DIRECT("DIRECT");
+    
+    companion object
+    {
+        fun fromString(mode: String): CoordinateMode
+        {
+            return values().find { it.modeName == mode } ?: HYBRID
+        }
+    }
+}
+
+/**
+ * Preprocessing technique enum
+ */
+enum class PreprocessingTechnique
+{
+    ULTRALYTICS,
+    ENHANCED,
+    SHARPENED,
+    ORIGINAL
+}

app/src/main/java/com/quillstudios/pokegoalshelper/ml/YOLOInferenceEngine.kt

@@ -27,7 +27,10 @@ import kotlin.math.min
  * - Weighted NMS and TTA
  * - Debug and testing features
  */
-class YOLOInferenceEngine(private val context: Context) : MLInferenceEngine
+class YOLOInferenceEngine(
+    private val context: Context,
+    private val config: YOLOConfig = YOLOConfig()
+) : MLInferenceEngine
 {
     companion object
     {
@@ -93,10 +96,10 @@ class YOLOInferenceEngine(private val context: Context) : MLInferenceEngine
     private var isInitialized = false
     
     // Shared thread pool for preprocessing operations (prevents creating new pools per detection)
-    private val preprocessingExecutor = Executors.newFixedThreadPool(3)
+    private val preprocessingExecutor = Executors.newFixedThreadPool(config.threadPoolSize)
     
-    private var confidenceThreshold = CONFIDENCE_THRESHOLD
-    private var classFilter: String? = null
+    private var confidenceThreshold = config.confidenceThreshold
+    private var classFilter: String? = config.classFilter
     
     // Performance tracking
     private var lastInferenceTime = 0L
@@ -215,8 +218,8 @@ class YOLOInferenceEngine(private val context: Context) : MLInferenceEngine
             ortEnvironment = OrtEnvironment.getEnvironment()
             
             // Copy model from assets to internal storage
-            Log.i(TAG, "📂 Copying model file: $MODEL_FILE")
-            val model_path = copyAssetToInternalStorage(MODEL_FILE)
+            Log.i(TAG, "📂 Copying model file: ${config.modelFile}")
+            val model_path = copyAssetToInternalStorage(config.modelFile)
             if (model_path == null)
             {
                 Log.e(TAG, "❌ Failed to copy ONNX model from assets")
@@ -493,7 +496,7 @@ class YOLOInferenceEngine(private val context: Context) : MLInferenceEngine
             Log.d(TAG, "🔧 Ultralytics preprocessing: input ${inputMat.cols()}x${inputMat.rows()}, type=${inputMat.type()}")
             
             // Step 1: Letterbox resize (preserves aspect ratio with padding)
-            val letterboxed = letterboxResize(inputMat, INPUT_SIZE, INPUT_SIZE)
+            val letterboxed = letterboxResize(inputMat, config.inputSize, config.inputSize)
             
             // Step 2: Apply slight noise reduction (Ultralytics uses this)
             val denoised = Mat()
@@ -628,7 +631,7 @@ class YOLOInferenceEngine(private val context: Context) : MLInferenceEngine
         val resized = Mat()
         try
         {
-            Imgproc.resize(inputMat, resized, Size(INPUT_SIZE.toDouble(), INPUT_SIZE.toDouble()))
+            Imgproc.resize(inputMat, resized, Size(config.inputSize.toDouble(), config.inputSize.toDouble()))
         }
         catch (e: Exception)
         {