1、目标检测：支持自己训练的模型推理

2、目标检测：支持yolo12模型
3、支持JDK8使用
4、引入离线依赖库，支持完全离线使用
5、优化FaceNet人脸比对速度
6、支持4通道图片检测

smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/config/DetectorModelConfig.java

@@ -1,6 +1,8 @@
-package cn.smartjavaai.objectdetection;
+package cn.smartjavaai.objectdetection.config;
 
 import cn.smartjavaai.common.enums.DeviceEnum;
+import cn.smartjavaai.objectdetection.constant.DetectorConstant;
+import cn.smartjavaai.objectdetection.enums.DetectorModelEnum;
 import lombok.Data;
 
 /**
@@ -20,13 +22,25 @@ public class DetectorModelConfig {
     /**
      * 置信度阈值
      */
-    private float threshold = DetectorConfig.DEFAULT_THRESHOLD;
+    private float threshold = DetectorConstant.DEFAULT_THRESHOLD;
 
     /**
      * 设备类型
      */
     private DeviceEnum device;
 
+    /**
+     * 模型路径
+     */
+    private String modelPath;
+
+    /**
+     * 候选框数量：默认为8400. 应设置0到8400之间的整数
+     * 用于性能优化的关键参数，它通过限制模型后处理阶段需要处理的候选框（bounding boxes）数量来提高推理速度
+     * 建议不低于1000
+     */
+    private int maxBox;
+
     public DetectorModelConfig() {
     }
 

smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/constant/DetectorConstant.java

@@ -1,14 +1,17 @@
-package cn.smartjavaai.objectdetection;
+package cn.smartjavaai.objectdetection.constant;
 
 /**
  * @author dwj
  * @date 2025/4/7
  */
-public class DetectorConfig {
+public class DetectorConstant {
 
     /**
      * 置信度阈值
      */
     public static final float DEFAULT_THRESHOLD = 0.5F;
 
+
+
+
 }

smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/CriteriaBuilderFactory.java

@@ -0,0 +1,43 @@
+package cn.smartjavaai.objectdetection.criteria;
+
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.output.DetectedObjects;
+import ai.djl.repository.zoo.Criteria;
+import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
+import cn.smartjavaai.objectdetection.enums.DetectorModelEnum;
+import cn.smartjavaai.objectdetection.exception.DetectionException;
+import org.apache.commons.lang3.StringUtils;
+
+/**
+ * Criteria构建工厂
+ * @author dwj
+ * @date 2025/5/14
+ */
+public class CriteriaBuilderFactory {
+
+    public static Criteria<Image, DetectedObjects> createCriteria(DetectorModelConfig config) {
+        //以下模型modelPath不允许为空
+        if(config.getModelEnum() == DetectorModelEnum.YOLOV8_OFFICIAL ||
+                config.getModelEnum() == DetectorModelEnum.YOLOV12_OFFICIAL ||
+                config.getModelEnum() == DetectorModelEnum.YOLOV8_CUSTOM ||
+                config.getModelEnum() == DetectorModelEnum.YOLOV12_CUSTOM){
+            if(StringUtils.isBlank(config.getModelPath())){
+                throw new DetectionException("modelPath is null");
+            }
+        }
+        switch (config.getModelEnum()) {
+            case YOLOV8_OFFICIAL:
+                return new YoloCriteriaBuilder().buildCriteria(config);
+            case YOLOV12_OFFICIAL:
+                return new YoloCriteriaBuilder().buildCriteria(config);
+            case YOLOV8_CUSTOM:
+                return new YoloCriteriaBuilder().buildCriteria(config);
+            case YOLOV12_CUSTOM:
+                return new YoloCriteriaBuilder().buildCriteria(config);
+            // 其他类型
+            default:
+                return new DJLModelCriteriaBuilder().buildCriteria(config);
+        }
+    }
+
+}

smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/CriteriaBuilderStrategy.java

@@ -0,0 +1,22 @@
+package cn.smartjavaai.objectdetection.criteria;
+
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.output.DetectedObjects;
+import ai.djl.repository.zoo.Criteria;
+import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
+
+/**
+ * 模型加载策略接口，用于根据不同模型类型构建对应的 DJL Criteria 实例
+ * @author dwj
+ * @date 2025/5/14
+ */
+public interface CriteriaBuilderStrategy {
+
+    /**
+     * 根据模型类型构建对应的 DJL Criteria 实例
+     * @param config
+     * @return
+     */
+    Criteria<Image, DetectedObjects> buildCriteria(DetectorModelConfig config);
+
+}

smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/DJLModelCriteriaBuilder.java

@@ -0,0 +1,40 @@
+package cn.smartjavaai.objectdetection.criteria;
+
+import ai.djl.Application;
+import ai.djl.Device;
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.output.DetectedObjects;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.training.util.ProgressBar;
+import cn.smartjavaai.common.enums.DeviceEnum;
+import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
+import cn.smartjavaai.objectdetection.constant.DetectorConstant;
+
+import java.util.Objects;
+
+/**
+ * DJL提供的Criteria 构建器
+ * @author dwj
+ * @date 2025/5/14
+ */
+public class DJLModelCriteriaBuilder implements CriteriaBuilderStrategy {
+
+    private static final String DJL_MODEL_PREFIX = "djl://";
+
+    @Override
+    public Criteria<Image, DetectedObjects> buildCriteria(DetectorModelConfig config) {
+        Device device = null;
+        if(!Objects.isNull(config.getDevice())){
+            device = config.getDevice() == DeviceEnum.CPU ? Device.cpu() : Device.gpu();
+        }
+        Criteria<Image, DetectedObjects> criteria = Criteria.builder()
+                .optApplication(Application.CV.OBJECT_DETECTION)
+                .setTypes(Image.class, DetectedObjects.class)
+                .optArgument("threshold", config.getThreshold() > 0 ? config.getThreshold() : DetectorConstant.DEFAULT_THRESHOLD)
+                .optModelUrls(DJL_MODEL_PREFIX + config.getModelEnum().getModelUri())
+                .optDevice(device)
+                .optProgress(new ProgressBar())
+                .build();
+        return criteria;
+    }
+}

smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/YoloCriteriaBuilder.java

@@ -0,0 +1,40 @@
+package cn.smartjavaai.objectdetection.criteria;
+
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.output.DetectedObjects;
+import ai.djl.modality.cv.translator.YoloV8TranslatorFactory;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.training.util.ProgressBar;
+import cn.smartjavaai.objectdetection.constant.DetectorConstant;
+import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
+
+import java.nio.file.Paths;
+
+/**
+ * YOLO模型Criteria 构建器
+ * @author dwj
+ * @date 2025/5/14
+ */
+public class YoloCriteriaBuilder implements CriteriaBuilderStrategy {
+    @Override
+    public Criteria<Image, DetectedObjects> buildCriteria(DetectorModelConfig config) {
+        Criteria.Builder criteriaBuilder = Criteria.builder()
+                .setTypes(Image.class, DetectedObjects.class)
+                //.optModelUrls("/Users/wenjie/Documents/develop/face_model/yolo")
+                .optModelPath(Paths.get(config.getModelPath()))
+                .optEngine("OnnxRuntime")
+                .optArgument("width", 640) //将输入图像的宽度缩放为 640 像素
+                .optArgument("height", 640)
+                .optArgument("resize", true)
+                .optArgument("toTensor", true)
+                .optArgument("applyRatio", true)
+                .optTranslatorFactory(new YoloV8TranslatorFactory())
+                .optProgress(new ProgressBar())
+                .optArgument("threshold", config.getThreshold() > 0 ? config.getThreshold() : DetectorConstant.DEFAULT_THRESHOLD);
+        if(config.getMaxBox() >  0){
+            criteriaBuilder.optArgument("maxBox", config.getMaxBox());
+        }
+        Criteria<Image, DetectedObjects> criteria = criteriaBuilder.build();
+        return criteria;
+    }
+}

smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/enums/DetectorModelEnum.java

@@ -1,4 +1,4 @@
-package cn.smartjavaai.objectdetection;
+package cn.smartjavaai.objectdetection.enums;
 
 /**
  * 目标检测模型枚举
@@ -34,7 +34,14 @@ public enum DetectorModelEnum {
     YOLO3_DARKNET_COCO_608("ai.djl.mxnet/yolo/0.0.1/yolo3_darknet_coco_608"),
     YOLO3_MOBILENET_COCO_320("ai.djl.mxnet/yolo/0.0.1/yolo3_mobilenet_coco_320"),
     YOLO3_MOBILENET_COCO_416("ai.djl.mxnet/yolo/0.0.1/yolo3_mobilenet_coco_416"),
-    YOLO3_MOBILENET_COCO_608("ai.djl.mxnet/yolo/0.0.1/yolo3_mobilenet_coco_608");
+    YOLO3_MOBILENET_COCO_608("ai.djl.mxnet/yolo/0.0.1/yolo3_mobilenet_coco_608"),
+
+    YOLOV12_OFFICIAL(""),
+    YOLOV8_OFFICIAL(""),
+
+    YOLOV8_CUSTOM(""),
+
+    YOLOV12_CUSTOM("");
 
     /**
      * 根据名称获取枚举 (忽略大小写和下划线变体)

smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/model/DetectorModel.java

@@ -1,41 +1,34 @@
 package cn.smartjavaai.objectdetection.model;
 
-import ai.djl.Application;
-import ai.djl.Device;
 import ai.djl.MalformedModelException;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.cv.Image;
 import ai.djl.modality.cv.ImageFactory;
 import ai.djl.modality.cv.output.DetectedObjects;
-import ai.djl.opencv.OpenCVImageFactory;
+import ai.djl.modality.cv.translator.YoloV8TranslatorFactory;
 import ai.djl.repository.zoo.Criteria;
 import ai.djl.repository.zoo.ModelNotFoundException;
 import ai.djl.repository.zoo.ZooModel;
 import ai.djl.training.util.ProgressBar;
-import ai.djl.translate.TranslateException;
 import cn.smartjavaai.common.entity.DetectionResponse;
-import cn.smartjavaai.common.enums.DeviceEnum;
-import cn.smartjavaai.common.pool.ModelPredictorPoolManager;
 import cn.smartjavaai.common.pool.PredictorFactory;
 import cn.smartjavaai.common.utils.FileUtils;
 import cn.smartjavaai.common.utils.ImageUtils;
 import cn.smartjavaai.common.utils.OpenCVUtils;
-import cn.smartjavaai.objectdetection.DetectorConfig;
-import cn.smartjavaai.objectdetection.DetectorModelConfig;
+import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
+import cn.smartjavaai.objectdetection.constant.DetectorConstant;
+import cn.smartjavaai.objectdetection.criteria.CriteriaBuilderFactory;
 import cn.smartjavaai.objectdetection.exception.DetectionException;
 import cn.smartjavaai.objectdetection.utils.DetectorUtils;
 import lombok.extern.slf4j.Slf4j;
-import org.apache.commons.lang3.Validate;
+import org.apache.commons.lang3.StringUtils;
 import org.apache.commons.pool2.ObjectPool;
 import org.apache.commons.pool2.impl.GenericObjectPool;
-import org.apache.commons.pool2.impl.GenericObjectPoolConfig;
 
 import javax.imageio.ImageIO;
 import java.awt.image.BufferedImage;
 import java.io.*;
-import java.nio.file.Path;
 import java.nio.file.Paths;
-import java.time.Duration;
 import java.util.Objects;
 
 /**
@@ -50,23 +43,16 @@ public class DetectorModel implements AutoCloseable{
 
     //private Predictor<Image, DetectedObjects> predictor;
 
-    private static final String DJL_MODEL_PREFIX = "djl://";
+
 
     private ObjectPool<Predictor<Image, DetectedObjects>> predictorPool;
 
     public void loadModel(DetectorModelConfig config){
-        Device device = null;
-        if(!Objects.isNull(config.getDevice())){
-            device = config.getDevice() == DeviceEnum.CPU ? Device.cpu() : Device.gpu();
+        if(Objects.isNull(config.getModelEnum())){
+            throw new DetectionException("未配置模型枚举");
         }
-        Criteria<Image, DetectedObjects> criteria = Criteria.builder()
-                        .optApplication(Application.CV.OBJECT_DETECTION)
-                        .setTypes(Image.class, DetectedObjects.class)
-                        .optArgument("threshold", config.getThreshold() > 0 ? config.getThreshold() : DetectorConfig.DEFAULT_THRESHOLD)
-                        .optModelUrls(DJL_MODEL_PREFIX + config.getModelEnum().getModelUri())
-                        .optDevice(device)
-                        .optProgress(new ProgressBar())
-                        .build();
+        Criteria<Image, DetectedObjects> criteria = CriteriaBuilderFactory.createCriteria(config);
+
         try {
             model = criteria.loadModel();
             // 创建池子：每个线程独享 Predictor

smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/model/ObjectDetectionModelFactory.java

@@ -1,12 +1,11 @@
 package cn.smartjavaai.objectdetection.model;
 
 import cn.smartjavaai.common.config.Config;
-import cn.smartjavaai.objectdetection.DetectorModelConfig;
-import cn.smartjavaai.objectdetection.DetectorModelEnum;
+import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
+import cn.smartjavaai.objectdetection.enums.DetectorModelEnum;
 import cn.smartjavaai.objectdetection.exception.DetectionException;
 import lombok.extern.slf4j.Slf4j;
 
-import java.util.Map;
 import java.util.Objects;
 import java.util.concurrent.ConcurrentHashMap;