基于YOLO目标检测模型的大规模生产电子元件自动化缺陷检测

<code class="python"># =============================================================================# Automated Defect Detection for Mass-Produced Electronic Components# Based on YOLO Object Detection Models - 伪代码实现# =============================================================================import cv2import numpy as npimport torchimport yolov5  # 或其他YOLO版本class ElectronicComponentDefectDetector:    def __init__(self, model_path, conf_threshold=0.5, iou_threshold=0.4):        """        初始化缺陷检测器        Args:            model_path: 预训练的YOLO模型路径            conf_threshold: 置信度阈值            iou_threshold: IoU阈值用于NMS        """        self.model = yolov5.load(model_path)        self.model.conf = conf_threshold        self.model.iou = iou_threshold        self.defect_classes = ['short_circuit', 'open_circuit', 'solder_void',                               'component_misalignment', 'crack', 'contamination']            def preprocess_image(self, image_path):        """        图像预处理        """        # 读取图像        image = cv2.imread(image_path)        # 转换为RGB        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)        # 图像增强 (可选)        enhanced_image = self.image_enhancement(image_rgb)        return enhanced_image        def image_enhancement(self, image):        """        图像增强以提高检测精度        """        # 直方图均衡化        lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)        lab[:,:,0] = cv2.equalizeHist(lab[:,:,0])        enhanced = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)                # 高斯模糊去噪        denoised = cv2.GaussianBlur(enhanced, (3,3), 0)                return denoised        def detect_defects(self, image_path):        """        执行缺陷检测        """        # 1. 图像预处理        processed_image = self.preprocess_image(image_path)                # 2. YOLO模型推理        results = self.model(processed_image)                # 3. 后处理        detections = results.pandas().xyxy[0]  # 获取检测结果                # 4. 分析检测结果        defect_analysis = self.analyze_defects(detections)                return detections, defect_analysis        def analyze_defects(self, detections):        """        分析缺陷检测结果        """        analysis = {            'total_defects': len(detections),            'defect_types': {},            'severity_level': 'PASS',            'defect_locations': []        }                for _, detection in detections.iterrows():            defect_class = detection['class']            confidence = detection['confidence']                        # 统计各类缺陷数量            if defect_class in analysis['defect_types']:                analysis['defect_types'][defect_class] += 1            else:                analysis['defect_types'][defect_class] = 1                        # 记录缺陷位置            bbox = {                'xmin': detection['xmin'],                'ymin': detection['ymin'],                'xmax': detection['xmax'],                'ymax': detection['ymax']            }            analysis['defect_locations'].append({                'class': defect_class,                'confidence': confidence,                'bbox': bbox            })                # 根据缺陷数量和类型确定严重程度        if analysis['total_defects'] > 3:            analysis['severity_level'] = 'CRITICAL'        elif analysis['total_defects'] > 0:            analysis['severity_level'] = 'WARNING'                return analysis        def visualize_results(self, image_path, detections, save_path=None):        """        可视化检测结果        """        image = cv2.imread(image_path)                for _, detection in detections.iterrows():            # 绘制边界框            xmin, ymin, xmax, ymax = int(detection['xmin']), int(detection['ymin']), \                                   int(detection['xmax']), int(detection['ymax'])                        # 根据缺陷类型选择颜色            color = self.get_defect_color(detection['class'])                        cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)                        # 添加标签            label = f"{detection['name']}: {detection['confidence']:.2f}"            cv2.putText(image, label, (xmin, ymin-10),                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)                if save_path:            cv2.imwrite(save_path, image)                return image        def get_defect_color(self, defect_class):        """        根据缺陷类型返回对应的颜色        """        color_map = {            'short_circuit': (0, 0, 255),      # 红色 - 严重缺陷            'open_circuit': (0, 165, 255),     # 橙色 - 严重缺陷            'crack': (0, 255, 255),           # 黄色 - 中等缺陷            'solder_void': (255, 255, 0),     # 青色 - 轻微缺陷            'component_misalignment': (255, 0, 0), # 蓝色 - 轻微缺陷            'contamination': (128, 0, 128)     # 紫色 - 轻微缺陷        }        return color_map.get(defect_class, (255, 255, 255))# =============================================================================# 训练过程的伪代码# =============================================================================def train_defect_detection_model():    """    训练YOLO缺陷检测模型    """    # 1. 数据准备    dataset_config = {        'train': 'dataset/train/images',        'val': 'dataset/val/images',        'test': 'dataset/test/images',        'nc': 6,  # 缺陷类别数量        'names': ['short_circuit', 'open_circuit', 'solder_void',                  'component_misalignment', 'crack', 'contamination']    }        # 2. 模型配置    model_config = {        'model_type': 'yolov5s',  # 可选择yolov5n, yolov5s, yolov5m, yolov5l, yolov5x        'img_size': 640,        'batch_size': 16,        'epochs': 100,        'learning_rate': 0.01,        'optimizer': 'SGD',        'momentum': 0.937,        'weight_decay': 0.0005    }        # 3. 数据增强配置    augmentation_config = {        'hsv_h': 0.015,  # 色调增强        'hsv_s': 0.7,    # 饱和度增强        'hsv_v': 0.4,    # 明度增强        'rotate': 10,    # 旋转角度        'translate': 0.1, # 平移        'scale': 0.5,    # 缩放        'shear': 0.0,    # 剪切        'flipud': 0.0,   # 上下翻转        'fliplr': 0.5,   # 左右翻转        'mosaic': 1.0,   # Mosaic数据增强        'mixup': 0.0     # Mixup数据增强    }        # 4. 训练过程    for epoch in range(model_config['epochs']):        # 训练阶段        model.train()        for batch_idx, (images, targets) in enumerate(train_loader):            # 前向传播            predictions = model(images)                        # 计算损失            loss = compute_loss(predictions, targets)                        # 反向传播            optimizer.zero_grad()            loss.backward()            optimizer.step()                        # 记录训练指标            log_training_metrics(loss, batch_idx)                # 验证阶段        model.eval()        with torch.no_grad():            for images, targets in val_loader:                predictions = model(images)                # 计算验证指标                calculate_validation_metrics(predictions, targets)                # 保存最佳模型        if is_best_model(current_metrics):            save_checkpoint(model, epoch, current_metrics)# =============================================================================# 主程序# =============================================================================def main():    """    主执行函数    """    # 初始化缺陷检测器    detector = ElectronicComponentDefectDetector(        model_path='best_defect_detection_model.pt',        conf_threshold=0.6,        iou_threshold=0.45    )        # 批量处理电子组件图像    component_images = [        'production_batch_001/component_001.jpg',        'production_batch_001/component_002.jpg',        # ... 更多图像    ]        results = []    for image_path in component_images:        # 执行缺陷检测        detections, analysis = detector.detect_defects(image_path)                # 可视化结果        result_image = detector.visualize_results(            image_path,             detections,             save_path=f'results/{os.path.basename(image_path)}'        )                # 保存检测结果        results.append({            'image_path': image_path,            'analysis': analysis,            'defect_count': analysis['total_defects'],            'severity': analysis['severity_level']        })                # 打印检测结果        print(f"图像: {image_path}")        print(f"缺陷数量: {analysis['total_defects']}")        print(f"严重程度: {analysis['severity_level']}")        print("---")        # 生成检测报告    generate_inspection_report(results)def generate_inspection_report(results):    """    生成质量检测报告    """    total_components = len(results)    defective_components = sum(1 for r in results if r['defect_count'] > 0)    yield_rate = (total_components - defective_components) / total_components * 100        print(f"=== 质量检测报告 ===")    print(f"总检测数量: {total_components}")    print(f"缺陷产品数量: {defective_components}")    print(f"良率: {yield_rate:.2f}%")        # 统计各类缺陷    defect_statistics = {}    for result in results:        for defect_type, count in result['analysis']['defect_types'].items():            if defect_type in defect_statistics:                defect_statistics[defect_type] += count            else:                defect_statistics[defect_type] = count        print("缺陷类型统计:")    for defect_type, count in defect_statistics.items():        print(f"  {defect_type}: {count}")if __name__ == "__main__":    main()</code>